1) Twitter¶
Данные вот отсюда: https://www.kaggle.com/crowdflower/twitter-airline-sentiment
In [1]:
import requests
import pandas as pd
import io
import numpy
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import sklearn
import seaborn as sns
import random
import warnings; warnings.filterwarnings(action='once')
from datetime import datetime
import plotly.express as px
from collections import Counter
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.metrics import mean_squared_error, mean_squared_log_error, r2_score
import math
import catboost as cb
from regressors import stats
/opt/anaconda3/lib/python3.7/site-packages/nbformat/notebooknode.py:4: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working /opt/anaconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject /opt/anaconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
In [2]:
large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
'legend.fontsize': med,
'figure.figsize': (16, 10),
'axes.labelsize': med,
'axes.titlesize': med,
'xtick.labelsize': med,
'ytick.labelsize': med,
'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline
Посмотрим на наши данные по твитам.
Основные столбцы в нём:
- airline sentiment (общее настроение твита),
- negativereason (причина негативности твита, если есть)
- airline (название авиакомпании)
- retweet_count (количество ретвитов)
- text (текст твита)
- tweer_created (время публикации)
- tweet_location (место публикации твита)
In [3]:
tweets = pd.read_csv('Tweets.csv')
tweets.head()
Out[3]:
| tweet_id | airline_sentiment | airline_sentiment_confidence | negativereason | negativereason_confidence | airline | airline_sentiment_gold | name | negativereason_gold | retweet_count | text | tweet_coord | tweet_created | tweet_location | user_timezone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 570306133677760513 | neutral | 1.0000 | NaN | NaN | Virgin America | NaN | cairdin | NaN | 0 | @VirginAmerica What @dhepburn said. | NaN | 2015-02-24 11:35:52 -0800 | NaN | Eastern Time (US & Canada) |
| 1 | 570301130888122368 | positive | 0.3486 | NaN | 0.0000 | Virgin America | NaN | jnardino | NaN | 0 | @VirginAmerica plus you've added commercials t... | NaN | 2015-02-24 11:15:59 -0800 | NaN | Pacific Time (US & Canada) |
| 2 | 570301083672813571 | neutral | 0.6837 | NaN | NaN | Virgin America | NaN | yvonnalynn | NaN | 0 | @VirginAmerica I didn't today... Must mean I n... | NaN | 2015-02-24 11:15:48 -0800 | Lets Play | Central Time (US & Canada) |
| 3 | 570301031407624196 | negative | 1.0000 | Bad Flight | 0.7033 | Virgin America | NaN | jnardino | NaN | 0 | @VirginAmerica it's really aggressive to blast... | NaN | 2015-02-24 11:15:36 -0800 | NaN | Pacific Time (US & Canada) |
| 4 | 570300817074462722 | negative | 1.0000 | Can't Tell | 1.0000 | Virgin America | NaN | jnardino | NaN | 0 | @VirginAmerica and it's a really big bad thing... | NaN | 2015-02-24 11:14:45 -0800 | NaN | Pacific Time (US & Canada) |
In [4]:
len(tweets)
Out[4]:
14640
Начнём предобработку данных. Вначале переведём время в прафильный формат
In [5]:
for i in range(len(tweets)):
tweets.loc[i, 'tweet_created'] = tweets.loc[i, 'tweet_created'][:-6]
tweets.loc[i, 'tweet_created'] = datetime.strptime(tweets.loc[i, 'tweet_created'], '%Y-%m-%d %H:%M:%S')
tweets.head()
Out[5]:
| tweet_id | airline_sentiment | airline_sentiment_confidence | negativereason | negativereason_confidence | airline | airline_sentiment_gold | name | negativereason_gold | retweet_count | text | tweet_coord | tweet_created | tweet_location | user_timezone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 570306133677760513 | neutral | 1.0000 | NaN | NaN | Virgin America | NaN | cairdin | NaN | 0 | @VirginAmerica What @dhepburn said. | NaN | 2015-02-24 11:35:52 | NaN | Eastern Time (US & Canada) |
| 1 | 570301130888122368 | positive | 0.3486 | NaN | 0.0000 | Virgin America | NaN | jnardino | NaN | 0 | @VirginAmerica plus you've added commercials t... | NaN | 2015-02-24 11:15:59 | NaN | Pacific Time (US & Canada) |
| 2 | 570301083672813571 | neutral | 0.6837 | NaN | NaN | Virgin America | NaN | yvonnalynn | NaN | 0 | @VirginAmerica I didn't today... Must mean I n... | NaN | 2015-02-24 11:15:48 | Lets Play | Central Time (US & Canada) |
| 3 | 570301031407624196 | negative | 1.0000 | Bad Flight | 0.7033 | Virgin America | NaN | jnardino | NaN | 0 | @VirginAmerica it's really aggressive to blast... | NaN | 2015-02-24 11:15:36 | NaN | Pacific Time (US & Canada) |
| 4 | 570300817074462722 | negative | 1.0000 | Can't Tell | 1.0000 | Virgin America | NaN | jnardino | NaN | 0 | @VirginAmerica and it's a really big bad thing... | NaN | 2015-02-24 11:14:45 | NaN | Pacific Time (US & Canada) |
Построим гистограммы всех интересных параметров
In [6]:
df = tweets.groupby('airline').size().reset_index(name='counts')
n = df['airline'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['airline'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})
plt.gca().set_xticklabels(df['airline'], rotation=60, horizontalalignment= 'right')
plt.title("Tweets about different airlines", fontsize=22)
plt.ylabel('# Tweets')
plt.show()
In [7]:
df = tweets.groupby('airline_sentiment').size().reset_index(name='counts')
fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)
data = df['counts']
categories = df['airline_sentiment']
def func(pct, allvals):
absolute = int(pct/100.*np.sum(allvals))
return "{:.1f}% ({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(data,
autopct=lambda pct: func(pct, data),
textprops=dict(color="w"),
colors=plt.cm.Dark2.colors,
startangle=140)
ax.legend(wedges, categories, loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
plt.setp(autotexts, size=10, weight=700)
ax.set_title("Tweet sentiment")
plt.show()
In [8]:
df = tweets.groupby('airline_sentiment').size().reset_index(name='counts')
n = df['airline_sentiment'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['airline_sentiment'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})
plt.gca().set_xticklabels(df['airline_sentiment'], rotation=60, horizontalalignment= 'right')
plt.title("Tweets with different attitudes", fontsize=22)
plt.ylabel('# Tweets')
plt.show()
In [9]:
df = tweets.groupby('negativereason').size().reset_index(name='counts')
n = df['negativereason'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['negativereason'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})
plt.gca().set_xticklabels(df['negativereason'], rotation=60, horizontalalignment= 'right')
plt.title("Tweets with different reasons for dissatisfaction", fontsize=22)
plt.ylabel('# Tweets')
plt.show()
Посчитаем, сколько люди писали твитов каждый день и построим по этому графики (в виде гистограммы и time-series). По ним можно будет узнать, в какие дни недели люди пишут активнее всего. От этого будет зависеть работа поддержки и обновление данных при решении этой задачи в реальной жизни
In [10]:
days = []
for i in range(len(tweets)):
days.append(tweets.iloc[i]['tweet_created'].day)
set(days)
Out[10]:
{16, 17, 18, 19, 20, 21, 22, 23, 24}
In [11]:
c = Counter(days)
plt.bar(c.keys(), c.values())
Out[11]:
<BarContainer object of 9 artists>
In [12]:
df = pd.DataFrame.from_dict(c, orient='index').reset_index()
df = df.rename(columns={"index": "Day", 0: "Number of tweets"})
df
Out[12]:
| Day | Number of tweets | |
|---|---|---|
| 0 | 24 | 1344 |
| 1 | 23 | 3028 |
| 2 | 22 | 3079 |
| 3 | 21 | 1557 |
| 4 | 20 | 1500 |
| 5 | 19 | 1376 |
| 6 | 18 | 1344 |
| 7 | 17 | 1408 |
| 8 | 16 | 4 |
In [13]:
fig = px.line(df, x='Day', y='Number of tweets')
fig.show()
22 и 23 число (на которые на графике приходится пик) - это были воскресенье и понедельник соответственно. Скорее всего люди активнее пишут в эти дни, потому что оставляют комментарии о своих полётах на выходных
Для тех же целей можно произвести аналогичный анализ по времени суток написания твитов
In [14]:
hours = []
for i in range(len(tweets)):
hours.append(tweets.iloc[i]['tweet_created'].hour)
set(hours)
Out[14]:
{0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23}
In [15]:
c = Counter(hours)
plt.bar(c.keys(), c.values())
Out[15]:
<BarContainer object of 24 artists>
In [16]:
df = pd.DataFrame.from_dict(c, orient='index').reset_index()
df = df.set_index('index')
df.sort_index(inplace=True)
df = df.reset_index()
df = df.rename(columns={"index": "Hour", 0: "Number of tweets"})
df.head()
Out[16]:
| Hour | Number of tweets | |
|---|---|---|
| 0 | 0 | 131 |
| 1 | 1 | 111 |
| 2 | 2 | 174 |
| 3 | 3 | 225 |
| 4 | 4 | 368 |
In [17]:
fig = px.line(df, x="Hour", y="Number of tweets")
fig.show()
Как мы видим, пик приходится на первую половину дня: с 8 до 14 часов
Всю ту же информацию можно визуализировать выборочно для одной из авиакомпаний, на которой нам нужно будет заострить внимание
In [18]:
tweets_united = tweets[tweets['airline'] == "United"].copy()
tweets_united.head()
Out[18]:
| tweet_id | airline_sentiment | airline_sentiment_confidence | negativereason | negativereason_confidence | airline | airline_sentiment_gold | name | negativereason_gold | retweet_count | text | tweet_coord | tweet_created | tweet_location | user_timezone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 504 | 570307876897628160 | positive | 1.0000 | NaN | NaN | United | NaN | rdowning76 | NaN | 0 | @united thanks | NaN | 2015-02-24 11:42:48 | usa | NaN |
| 505 | 570307847281614848 | positive | 1.0000 | NaN | NaN | United | NaN | CoreyAStewart | NaN | 0 | @united Thanks for taking care of that MR!! Ha... | NaN | 2015-02-24 11:42:41 | Richmond, VA | Eastern Time (US & Canada) |
| 506 | 570307109704900608 | negative | 1.0000 | Cancelled Flight | 0.703 | United | NaN | CoralReefer420 | NaN | 0 | @united still no refund or word via DM. Please... | NaN | 2015-02-24 11:39:45 | Bay Area, California | Alaska |
| 507 | 570307026263384064 | negative | 1.0000 | Late Flight | 1.000 | United | NaN | lsalazarll | NaN | 0 | @united Delayed due to lack of crew and now de... | NaN | 2015-02-24 11:39:25 | NaN | Mountain Time (US & Canada) |
| 508 | 570306733010264064 | positive | 0.3441 | NaN | 0.000 | United | NaN | rombaa | NaN | 0 | @united thanks -- we filled it out. How's our ... | NaN | 2015-02-24 11:38:15 | NaN | NaN |
In [19]:
days = []
for i in range(len(tweets_united)):
days.append(tweets.iloc[i]['tweet_created'].day)
set(days)
Out[19]:
{17, 18, 19, 20, 21, 22, 23, 24}
In [20]:
c = Counter(days)
df = pd.DataFrame.from_dict(c, orient='index').reset_index()
df = df.rename(columns={"index": "Day", 0: "Number of tweets"})
df['Number of tweets'].mean()
Out[20]:
477.75
Можно также внимательнее посмотреть на место создание твитов. Они написаны в разном формате, но можно попробовать вытащить твиты из Нью-Йорка, например, по разным его сокращениям
In [21]:
for i in tweets['tweet_location']:
print(i)
nan
nan
Lets Play
nan
nan
nan
San Francisco CA
Los Angeles
San Diego
Los Angeles
1/1 loner squad
NYC
NYC
nan
San Francisco, CA
palo alto, ca
west covina
this place called NYC
Somewhere celebrating life.
Boston | Waltham
nan
nan
Los Angeles
Boston, MA
714
nan
nan
San Francisco, CA
San Mateo, CA & Las Vegas, NV
Brooklyn
nan
California, San Francisco
Washington DC
nan
Texas
Worldwide
Central Texas
Central Texas
i'm creating a monster
San Francisco, CA
nan
Iowa City
Los Angeles
Georgia
nan
Los Angeles
Turks and caicos
Oakland via Midwest
New York, NY
nan
Worldwide
Northern Virginia
Los Angeles / Atlanta
nan
nan
new york, new york
brooklyn, Ny
Bali, Republic of Indonesia
UK, USA.
Gold Coast, Australia
Stockton, CA
New York, NY
nan
Twin Cities, Minn.
nan
USA
next city
SF ↔ NY
New York, NY
New York + Panama
San Francisco, CA
Los Angeles
London, England
Los Angeles
Floridian from Cincinnati
Dallas, Texas
USA
Dallas, Texas
nan
nan
nan
Seattle, WA
Los Angeles
nan
nan
Lower Pacific Heights, SF, CA
Chicago
nan
Los Cabos,Mexico
New York, NY
nan
nan
nan
nan
nan
Los Angeles, CA
Austin, TX
nan
Sterling Heights, MI
Washington DC
Manhattan Beach, CA
nan
Greater Los Angeles
Floridian from Cincinnati
nan
Online
nan
nan
Dallas, TX
Washington, DC
Seattle
New York City, NY
Los Angeles, CA
nan
Earth
nan
Halifax, Nova Scotia, Canada
Los Angeles, CA
Near a park, water, or lights
USA
San Francisco CA
San Francisco CA
nan
San Francisco
nan
New York
NYC
nan
San Francisco, CA
nan
nan
nan
Sacramento,California
nan
San Diego
Oakland, California
nan
nan
All Over!
Providence, RI
San Diego
Los Angeles, CA
New York City
New York
All Over!
iPhone: 29.741360,-90.131523
Los Angeles
hollywood, california
Washington, DC
Las Vegas
Las Vegas
nan
San Francisco
nan
nan
nan
nan
San Francisco
CA
@ the moment in New Delhi
Brooklyn
Brooklyn
Washington, DC
Los Angeles
nan
Washington, DC
FL410
Tokyo
Philadelphia, Pa
FL410
FL410
nan
FL410
Belmar, NJ
nan
Las Vegas
Barbados
nan
nan
Las Vegas, NV
nan
Northern Virginia
Boston, MA
Worldwide
LaLa Land
nan
nan
Las Vegas, NV
Chicago
DC - LA - Venice Beach
Argentina
New York, NY
nan
BAYAREA✈️NYC
Washington, DC
New York City
BAYAREA✈️NYC
MA // Ashton in Wonderland
New York City
nan
Washington, DC
Los Angeles, California
Los Angeles, California
CT
Los Angeles, California
FREE ADVICE!
Natick, MA, USA
okayville, population me
nan
CT
CT
FREE ADVICE!
New York, NY
nan
Austin, TX
Silicon Valley, California
Silicon Valley, California
New York, NY
New York, NY
chile
California, USA
California, USA
nan
nan
nan
San Francisco
SF Bay Area
Los Angeles, CA
nan
Los Angeles, CA
Worldwide
Massachusetts
Los Angeles, CA
New York
Las Vegas
Massachusetts
nan
nan
nan
CALIFORNIA, USA
downtown, CA
Brooklyn
Cambridge, MA, USA, Earth
Somewhere outside of Fenway
Abuja
chile
Los Angeles, CA
Las Piedras, Peru
Charlestown, MA
nan
San Francisco
Las Vegas
New York for now.
Albuquerque, NM
#SF#Optimist #Freefalling
Global
Texas
Washington State
USA
Los Angeles, CA
Florida
Florida
San Francisco
Montreal
Orange County, CA
San Francisco
nan
Global
Phoenix AZ
Bay Area, CA
nan
nan
Boca Raton FL 33434
Follow/Retweet for giveaways!
Los Angeles
Global
Anaheim, CA
Follow/Retweet for giveaways!
Malibu, CA
Las Vegas, NV
nan
NY, NY
Rijswijk ♡ The Netherlands
NY, NY
Portland, Maine
Kansas City, MO
Las Vegas, NV
nan
dc
San Francisco, CA
Las Vegas, NV
Global
Portland, Maine
San Francisco
Global
nan
USA
Global
New York, NY
nan
Los Angeles
San Francisco
Brooklyn, NY
New York, NY
nan
Massachusetts
Hoboken, NJ
New York City
San Francisco, CA
New York City
California 92705
39.149054, -77.273589
New york
BOS • LA • NYC
Brooklyn, NY
New York City
Brooklyn, NY
San Francisco, CA
nan
nan
San Francisco, CA
San Francisco, CA
nan
Dallas, TX
Dutchess County, NY
Bay Area
Dutchess County, NY
Dutchess County, NY
Bay Area
San Francisco
San Francisco
Wandering So-Cal-ian
At an airport near you....
Wandering So-Cal-ian
nan
San Francisco
freyabevanfund@hotmail.com
San Francisco
Northern Virginia
Bay Area
freyabevanfund@hotmail.com
Portland, OR
SD/LA/PS
nan
DC | Jersey City
nan
Telluride, CO
Portland, Oregon
san francisco
Vancouver, WA
”Straight Outta Jersey”
nan
Phoenix AZ
LES, NYC
LES, NYC
san francisco
Global
new york
nan
The World
nan
VFILES DJ CHAMPION
Chicago
☂ Seattle | WA
3rd Planet from the Sun
San Francisco, CA
nan
CT
CT
San Francisco
San Francisco
Global
San Francisco
nan
all over ca
San Francisco
S F
NY, NJ, CA, Greece
Long Island, NY
ATX
Out to Brunch with @Kanyewest.
San Francisco
San Francisco, CA
San Francisco, CA
East Village, New York City
Birmingham, Michigan
Charlotte, NC
California
Doha, Qatar
Libya
Algiers, Algeria
mckinney, tx
Out to Brunch with @Kanyewest.
nan
nan
your third eye...
The other side
714 to 972
New York, sort of!
New York, sort of!
nan
Washington D.C.
United States
Southern California
Austin, TX
Hogwarts
nan
Long Beach, CA
nan
Staten Island, NY
CT
nan
CT
St. Francis (Calif.)
CT
California, San Francisco
New York City
CT
San Francisco, CA
Washington, D.C.
California Love #VFL
nan
Austin, Texas
Austin, TX
nan
San Francisco, CA
NJ USA
San Francisco, CA
nan
Annapolis, Maryland
nan
Abu Dhabi
San Marcos, CA
New York, NY
USA
nan
austin dallas chile london
Dallas, TX
San Jose, California
San Diego, CA
Zip City aka Dallas, TX
nan
nan
nan
Boston, MA
nan
nan
nan
nan
nan
San Francisco
nan
nan
USA
Hamilton, Ohio
Dallas, TX
Dallas, TX
Texas
Dallas, Texas
Dallas, Texas
Cork.Ireland
nan
nan
could be anywhere
Worldwide
Oakland, CA
Los Angeles, CA
Oakland, CA
Los Angeles, CA
SF Bay Area/ Las Vegas
nan
Hollywood, FL
Oakland, Ca
New York x Long Island
On a bar floor in Denver.
Waltham, MA
nan
Jersey City, New Jersey
Washington, DC
Los Angeles, CA
New York City
nan
nan
P H I L A D E L P H I A
nan
nan
nan
Charlottesville, VA
next city
Charlottesville, VA
next city
Texas
nan
Fargo, ND ( & Tucson, AZ)
LA&OC
Washington, DC
Texas
nan
Los Angeles, CA
San Franciso, CA
Lake Oswego, Oregon
Chicago
Living in a Gangsters Paradise
Las Vegas, NV
usa
Richmond, VA
Bay Area, California
nan
nan
Erie, PA
Kilmarnock, now Edinburgh.
New York
Berkeley Heights, NJ
Columbus, Ohio
Kilmarnock, now Edinburgh.
USA
Portland, OR
Harrisburg
Las Vegas
nan
nan
Brooklin, New York
USA
nan
nan
USA
nan
New Jersey/Maryland
USA
USA
Parkersburg, WV
South Carolina
nan
NYC
USA
USA
nan
Parkersburg, WV
nan
nan
Tampa
Washington, DC
nan
USA
DC
DC
btv
Portland, OR
nan
NYC
Houston, TX
Los Angeles
The Center of My Universe
California/Nevada
Kansas City, MO
nan
nan
nan
Astoria, OR
Colorado Springs, Colorado
San Francisco Bay Area
945XX to 111XX
nan
Austin, TX
The Center of My Universe
Jerusalem, Israel
Jerusalem, Israel
Jacksonville, FL
nan
Jacksonville, FL
nan
nan
nan
nan
nan
nan
Kansas City, MO
nan
nan
nan
nan
nan
Harrisburg
Wall Street • Manhattan • NYC
nan
nan
nan
nan
nan
Austin · LA · London · NY · SF
nan
nan
Louisville
Sterling Heights, MI
nan
nan
nan
Steamboat Springs, CO
btv
Above the B/D/N/Q/R/2/3/4/5
searching for coffee
Chicago, IL, USA
nyc to la
nan
nan
nan
nan
nan
Washington, D.C. Metro Area
nan
nan
Richmond, VA
Connecticut
Miami, New York, Boston
Seaside, California
new york, baby
nan
Canada
NYC
Midwest
nan
Marlow, UK
nan
Summit, NJ
NYC
Princeton, NJ
Hoops City (Memphis, TN)
Europe
Right Now Mostly The Hospital
Manchester
Midwest
Seattle
Seattle
Seattle
Seattle
nan
Seattle
Seattle
Seattle
Princeton, NJ
Chicago, IL
Kansas
Above the B/D/N/Q/R/2/3/4/5
New Jersey
Global
Luxembourg
.
.
nan
nan
nan
Larne
btv
nan
nan
Boulder, CO
Princeton, NJ
nan
nan
nan
nan
Larne
nan
nan
Southern California
Chicago, IL, USA
nan
nan
nan
nan
searching for coffee
nan
Seattle
Seattle
Seattle
nan
Seattle
Seattle
nan
Brooklyn
Glasgow
searching for coffee
nan
nan
Seattle
Seattle
nyc to la
Alphen aan den Rijn
nan
nan
nan
London baby
nan
nan
Midwest
nan
Nottingham
Nottingham
mentor, Ohio
Saipan, MP
Nottingham
mentor, Ohio
nan
Nottingham
nan
nan
nan
Seoul City to LA
nan
New York City, NY
iPhone: 55.946030,-3.189382
New York
nan
Nottingham
mentor, Ohio
nan
nan
bonkers in Yonkers
Vancouver BC
nan
nan
Stockholm, Sweden
bonkers in Yonkers
Madison, NJ
CORGaming@cellucor.com
mentor, Ohio
Brighton, UK
Brighton, UK
nan
All over, but mostly NorCal
nan
nan
nan
New Jersey
Ottawa, Canada
nan
Huntsville AL USA
Ottawa, Canada
nan
Huntsville AL USA
New Jersey
San Francisco Bay Area
Huntsville AL USA
nan
San Francisco, CA
Washington, DC
New Jersey
Iowa
nan
Ottawa, Canada
Ottawa, Canada
Ottawa, Canada
Boulder, CO
Madison, NJ
Ottawa, Canada
nan
Rochester, NY
Chicago / Orlando / airplanes
nan
Madison, NJ
Madison, NJ
nan
CORGaming@cellucor.com
nan
Boulder, CO
Iowa
nan
Texas
nan
nan
WX report for 41 Mile, Hwy 50
nan
Missouri
nan
NYC
San Francisco
San Francisco
NY
Madison, NJ
nan
Brooklyn, NY
nan
New York City, NY
Ottawa, Canada
[Colorado]
mentor, Ohio
nan
San Francisco, CA
nan
Ottawa, Canada
WX report for 41 Mile, Hwy 50
Washington, DC
Ottawa, Canada
WX report for 41 Mile, Hwy 50
nan
nan
nan
Texas
nan
Colorado
nan
WX report for 41 Mile, Hwy 50
mentor, Ohio
San Francisco, CA
Chicago, IL
Rocklin, CA
Iowa
nan
nan
Alphen aan den Rijn
Singapore
Ottawa, Canada
nan
nan
Rocklin, CA
NY
Westerville, Ohio
Westerville, Ohio
Pacific Northwest
Cleveland, OH
Hermosa Beach, CA
mentor, Ohio
nan
nan
Los Vancouver, CA
nan
Broad & Pattison
Cornfields
nan
Chicago, IL
nan
nan
nan
nan
nan
nan
nan
nan
San Francisco
Michigan
Gainesville GA
Oregon
iPhone: 42.734546,-84.483753
nan
Brooklyn, NY
Chicago / Orlando / airplanes
Denver, CO
Brooklyn, NY
Chicago, IL
nan
Chicago, IL
nan
nan
iPhone: 39.201706,-106.854080
Denver, CO
Brooklyn, NY
San Francisco
Global
San Francisco
Global
DEN
DC949
Global
Raleigh, NC
CA
nan
nan
Dallas
#Omaha
#Omaha
Brooklyn, NY
nan
nan
San Francisco, CA
Colorado Springs, Colorado USA
Global
Ottawa
Silicon Valley
Lees Summit
usa
North Texas, USA
Dallas
nan
Colorado Springs, Colorado USA
Colorado Springs, Colorado USA
Houston, TX
nan
Global
nan
Honeoye Falls
nan
california
nan
GEM HQ (aka walk-in closet) NY
nan
nan
nan
Chicago, IL
nan
Cleveland, OH
Houston, TX
Cleveland, OH
Boston
Houston, TX
GEM HQ (aka walk-in closet) NY
Austin
GEM HQ (aka walk-in closet) NY
san diego
nan
san diego
GEM HQ (aka walk-in closet) NY
nan
nan
melbourne, australia
Chicago, IL
nan
Houston, TX
San Francisco, CA
nan
Brooklyn
Fort Worth, Texas
Winterfell
california
nan
Coventry, Connecticut
Fort Worth, Texas
nan
nan
Ausvegas
Nashville, TN
Santiago, Chile
nan
nan
San Francisco
New York City, NY
New York City, NY
nan
New York City, NY
All over the damn place.
Houston, TX
New York City, NY
New York City, NY
nan
nan
Liverpool, UK
San Francisco, California, USA
nan
nan
Brooklyn
nan
nan
Washington, DC
nan
New York
Washington, DC
Washington, DC
lalaland
lalaland
san francisco
nan
lalaland
lalaland
Morristown, nj
Chicago & traveling the world
Madrid (Spain)
Killington, Vt
Chicago & traveling the world
West Hollywood, CA
Roatan, Islas de la Bahia
nan
Semiahmoo, WA Soonerland
Austin, TX (Easton, PA born)
San Francisco
San Francisco
nan
Punk is the preacher.
nan
San Antonio, Texas
Eatontown, NJ
San Antonio, Texas
Buffalo/Oakland/Savannah/Ire
nan
Brighton, UK
nan
San Francisco, CA
Rotterdam, the Netherlands
West Hollywood, CA
San Francisco Bay Area
Washington, DC
San Francisco Bay Area
Swooning in New York, New York
nan
san diego
Brooklyn
Grand Rapids
Colorado
San Francisco Bay Area
mets hell
NJ/NYC
nan
nan
nan
nan
San Francisco Bay Area
Ottawa, Ontario
London UK & USA
Vancouver/Beverly Hills
Little Rock, AR
Harlem, NYC
New York City, NY
Grand Rapids
IL
Ottawa, Ontario
Roatan, Islas de la Bahia
nan
San Francisco Bay Area
Indiana
nan
Houston, TX
NJ
Nashville, TN
Washington DC
Brunswick, Ohio
Kingston, Ontario
Washington, DC
nan
with my brothers
New York, NY
New York City, NY
nan
nan
Atlanta
One of the C-gates at EWR.
Kona coast/Boston/Milan
Brunswick, Ohio
Edmonton International Airport
Washington, DC
Oakland by way of Chicago
nan
Mexico City
Harlem, NYC
San Francisco CA
New Jersey, USA
Oakland, CA
nan
founder @IndiraCollection
nan
Edison, NJ
Kona coast/Boston/Milan
nan
nan
Las Vegas
Houston, TX
Oakland, CA
Santa Rosa, CA
Long Island, NY
Oakland, CA
Brighton, UK
nan
nan
#Omaha
#Omaha
nan
Washington D.C.
San Francisco
nan
Pittsburgh area
PDX
PDX
Stockholm, Sweden
San Francisco CA
Winterfell
Stockholm, Sweden
nan
Fort Wayne, Indiana
Washington D.C.
Fort Wayne, Indiana
Wherever I want to be
Fort Wayne, Indiana
nan
Kona coast/Boston/Milan
San Francisco, CA
Vancouver BC
nan
SF, CA
nan
Scotland
Pittsburgh area
New York
Indiana
NEIN, London, or Maui.
Kingston, Ontario
Ottawa, Ontario
Scotland
NEIN, London, or Maui.
Chicago
nan
NEIN, London, or Maui.
NEIN, London, or Maui.
Mexico City
St. Louis, MO
NEIN, London, or Maui.
Rochester, NY
New Haven, CT
Pennsylvania
Scotland
SF mostly, NYC & London often.
Downtown Victoria
Chicago Illinois Crime.Inc
Cali,Colombia
Kona coast/Boston/Milan
CT, NY and most other states
East Coast, US
Chicago Illinois Crime.Inc
Los Angeles
nan
Chicago Illinois Crime.Inc
Stockholm, Sweden
CT, NY and most other states
CT, NY and most other states
nan
nan
nan
nan
nan
nan
nan
nan
Los Angeles
North Suburbs, IL (Chicago)
nan
Pittsburgh, PA
nan
Downtown Victoria
nan
Bay Area, CA
Cleveland, Ohio
Toronto, Canada
Pittsburgh, PA
ÜT: 34.078171,-118.285555
ÜT: 34.078171,-118.285555
nan
Fort Wayne, Indiana
Grand Rapids
Grand Rapids
Grand Rapids
Grand Rapids
Grand Rapids
Ottawa, Ontario
nan
nan
CT, NY and most other states
H-town
Toronto, ON.....for now.
nan
nan
nan
nan
nan
Arlington, VA
Taipei
CT, NY and most other states
USA
Taipei
Burlington, Vermont
Taipei
nan
Taipei
Taipei
Taipei
nan
New Haven, CT
nan
nan
Toronto, ON.....for now.
New Haven, CT
North Suburbs, IL (Chicago)
Georgia
San Diego, CA
california
nan
Chicago
Santiago, Chile
nan
New Haven, CT
Bardstown, KY
nan
nan
nan
Research Triangle, NC
nan
Ashtabula, OH
Colorado
Colorado
San Francisco Bay Area
Santiago, Chile
North Suburbs, IL (Chicago)
Mountlake Terrace, WA
ÜT: 37.427592,-122.116357
Chicago
New Jersey
New York
nan
Hilo, HI
Washington, DC
Los Angeles, CA
Kansas City, Missouri
Ashburn, VA
Coventry, Connecticut
Stumptown, Baby!
New York
nan
Frequently DC/NYC/San Diego
Needham, MA
Milwaukee, WI
Princeton, NJ
nan
Burlington, Vermont
Maui, Hawaii
San Francisco, California
nan
nan
nan
nan
Williamsburg, VA
syracuse
Brussels
Abu Dhabi
Maui, Hawaii
Barquisimeto - Maracaibo
nan
Calabasas, California
Abu Dhabi
Abu Dhabi
Cambridge, MA
Albuquerque, New Mexico
canton mi
Ashburn, VA
chicago
nan
New York
nan
Always around
Frequently DC/NYC/San Diego
Nashville, TN
nan
USA / The World
nan
Washington, DC
nan
Punk is the preacher.
Louisville
New Jersey
Louisville
nan
Louisville
Sheboygan
Stumptown, Baby!
Stumptown, Baby!
Punk is the preacher.
New York
Needham, MA
Dartmouth, MA
New York
New Haven, CT
GLP
nan
syracuse
Houston, Texas
Dartmouth, MA
nan
San Jose, CA
Kansas City, USA
nan
Punk is the preacher.
Punk is the preacher.
nan
London
Salt Lake City, Utah
chicago
Louisville
Frequently DC/NYC/San Diego
Brussels
nan
Weston super Mare
nan
Arlington, VA
nan
Arlington, VA
ÜT: 50.97861,-114.000116
ÜT: 50.97861,-114.000116
Collierville, TN USA
Coventry, Connecticut
USA
nan
Chicago
iPhone: -33.875538,151.196533
Punk is the preacher.
Louisville
nan
nan
Sheboygan
Boulder, Colorado
Sheboygan
Western Massachusetts, USA
ÜT: 50.97861,-114.000116
USA / The World
ÜT: 50.97861,-114.000116
ÜT: 50.97861,-114.000116
New York
New Jersey
ÜT: 50.97861,-114.000116
Hurst, TX
St. Louis, MO
Brossard
Stavromula Beta
USA / The World
USA / The World
nan
nan
Denver, CO
Chorley, Lancashire
nan
Sheboygan
Boston
Dallas, Texas
nan
P.R.O.B.
Roma
East Coast
nan
Park Slope
Lewis Center, OH
Richmond, VA
nan
nan
DOM-ination
nan
Wilmington, Delaware
Buffalo/Oakland/Savannah/Ire
nan
US
nan
Beijing
Cleveland, Ohio
North West of England
nan
San Francisco
nan
nan
San Francisco, CA
nan
Brownsville, Tx
nan
nan
Raleigh, NC
Indiana
New York/New Jersey
New York/New Jersey
Lexington, Ky
New York/New Jersey depends on
Los Angeles
nan
nan
nan
Portland, OR
Western Massachusetts, USA
London baby
nan
176 Thomas Johnson Drive, #204
Toronto
Portland, OR
nan
nan
nan
nan
nan
Bay Area, CA
San Francisco, Ca
nan
SD || CA
Vancouver, CA
nan
nan
Vancouver, CA
Vancouver, CA
Vancouver, CA
nan
nan
New York/New Jersey depends on
nan
Bay Area, CA
Bay Area, CA
London
New Haven, CT
Los Angeles, CA
nan
Boston, MA
Boston, MA
nan
nan
St. Louis MO
nan
Los Angeles
nan
Los Angeles
nan
EP TX
Grand Rapids
nan
nan
Washington D.C.
nan
nan
Buffalo, Ny
Canada
nan
Chicago
ÜT: 31.333857,-94.724549
nan
Danville CA
H-town
nan
H-town
Chicago
H-town
Monkton, Maryland
H-town
Rose Bubble
H-town
H-town
nan
San Francisco, Ca
Brooklyn, NY
nan
52.315477,4.995852
New Jersey
nan
Austin
#ygk
nan
Waltham, MA
Tampa, Florida
San Francisco, Ca
nan
Vancouver, CA
nan
Bay Area, CA
Toronto Area
nan
Brossard
GLP
the cupboard under the stairs
nan
nan
GLP
GLP
GLP
USA / The World
New Jersey
nan
Danville CA
Bay Area, CA
nan
nan
Hermosa Beach, CA
nan
nan
nan
nan
Beijing, China
Monkton, Maryland
nan
nan
nan
Wilmington, Delaware
san francisco, ca
nan
Medford, MA
Chicago
nan
nan
Rockaway, NJ
nan
nan
Akron, Ohio
Houston, TX
nan
nan
CO Springs, Occupied Colorado
nan
ÜT: 33.987103,-118.399024
Mexico City
Palo Alto, CA
Utah
New York & Washington, D.C.
Boulder, Co
Ireland
Utah
ÜT: 33.987103,-118.399024
nan
ÜT: 33.987103,-118.399024
San Francisco, California
San Francisco, California
New Jersey
Kansas City, Missouri
Hilo, HI
nan
nan
nan
nan
San Francisco
Houston, TX
nan
Hillsborough, NC
nan
Toronto
Lexington, Ky
Vancouver, WA
Hilo, HI
Hilo, HI
Los Angeles, CA
nan
nan
nan
nan
nan
Michigan
Queens, NY
Iowa City, IA
nan
nan
nan
nan
Los Angeles, CA
nan
nan
nan
Chapel Hill, NC
Charleston, WV
Albuquerque, New Mexico
Albuquerque, New Mexico
nan
Utah
Everywhere
Vienna, VA
CO Springs, Occupied Colorado
Mexico City
nan
Michigan
Oakland, CA
Home
nan
Stavromula Beta
nan
San Francisco, CA
NYC/Secaucus
Chicago
Toronto Canada
New York
North
Cincinnati, OH
Portland, OR
Chicago, IL
nan
ÜT: 40.729379,-74.002951
nan
Long Island
nan
Brooklyn, NY
nan
nan
nan
Kingston, Canada
nan
nan
Pittsburgh, PA
nan
Cambridge, MA
Vancouver, WA
nan
Harrisburg
nan
The Town
Martinsburg,WV
New York | Chicago
driving, probably
Los Angeles, CA
moving forward...
michigan
nan
nan
San Francisco, CA
nan
Kingston, Canada
Northern Ireland
nan
nan
nan
nan
nan
nan
San Francisco, CA
earth
DC
San Francisco, CA
STL
San Francisco, CA
nan
Kingston, Canada
Washington, D.C.
nan
earth
Chicago
earth
Nederland
nan
nan
Kingston, Canada
nan
Canada
Earth
Saint Louis
Saint Louis
Saint Louis
New York
Buffalo, NY
Los Angeles
Kingston, Canada
nan
nan
Los Angeles
Chicago
Pittsburgh
Chicago
nan
Pittsburgh
nan
Los Angeles
Chicago, IL
Hilo, HI
Chicago
nan
nan
nan
Leesburg, VA, USA
Toronto Area
nan
Winterfell
Everywhere
nan
Corpus Christi
Cambridge, MA
nan
nan
driving, probably
nan
Pittsburgh, PA
Global- Mostly Silicon Valley
Chicago, Illinois
ÜT: 41.88849,-87.6282
Shreveport, Louisiana
Portland, Zurich, Richmond
Vienna, VA
Chicagoland
nan
Toronto
nan
Europe - America
nan
ÜT: 41.88849,-87.6282
London, ON
nan
ÜT: 39.39339,-76.762595
New York
Medford, MA
Greensboro, NC
New York | Chicago
nan
Bay Area, CA
nan
Bedford, NH
Boston
Boston
nan
Richmond Virginia
Houston, Texas
Corpus Christi
Corpus Christi
Boston
nan
Corpus Christi
Corpus Christi
WASHINGTON HEIGHTS, NYC
nan
ÜT: 50.97861,-114.000116
Ft Collins
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
Houston, TX
nan
STL
Houston, TX
STL
Earth
nan
nan
nan
Los Angeles
nan
NYC
Solent - Hamble, Lymington
Erie, PA
Brooklyn, NY
San Francisco Bay Area
Watertown, MA
Los Angeles
Toronto Area
nan
nan
New York, NY
New York, NY
nan
new york, baby
anywhere but CStat
Pittsburgh, PA
Oak Park, IL
Aylesbury United Kingdom
Watertown, MA
nan
Chicago, IL
nan
ÜT: 42.986193,-87.914855
nan
Washington, DC via Nebraska
nan
nan
Global- Mostly Silicon Valley
nan
Bedford, NH
nan
Brooklyn, NY
nan
nan
Ireland
Ft Collins
Ft Collins
Cheltenham
Melbourne, Australia
nan
nan
Chicago
Massachusetts
QCϟDC
nan
Saskatoon
nan
Phoneix, AZ
London, ON
nan
nan
Lewis Center, OH
nan
nan
New York NY
nan
nan
nan
nan
nan
nan
STL
SE AZ By Way of HV
Lewis Center, OH
New York, NY
Brandon, Mississippi
Morton, IL
nan
nan
nan
nan
nan
deep in the ❤ of Texas...
nan
nan
Michigan
nan
San Francisco Bay Area
nan
STL
Boston, MA
SE AZ By Way of HV
Mountain View, CA
nan
?????
nan
Houston, TX
nan
nan
Dirty Jerz
Nueva York
San Francisco
Nueva York
Nueva York
Everywhere
Nueva York
San Francisco, CA
Ottawa, Ontario
Washington, DC
nan
California, USA
Lees Summit
Stavanger, Norway
Stavanger, Norway
Earth
nan
San Francisco
nan
Southern California and Hawaii
New Hartley
New York, USA
deep in the ❤ of Texas...
Southern California and Hawaii
Hingham, MA
Earth
deep in the ❤ of Texas...
Dallas
Loveland, Ohio
New York City
nan
nan
deep in the ❤ of Texas...
Kingwood
West Columbia,SC
Houston, TX
New York City
nan
Stavanger, Norway
Stavanger, Norway
Houston, TX
Quito, Ecuador
Austin, TX
201-600-2130
Phoneix, AZ
Dallas
nan
nan
ORLANDO, FL
Keyport, New Jersey
nan
deep in the ❤ of Texas...
Fredericksburg, VA
nan
nan
Toronto
nan
nan
Kingwood
Kingwood
earth
nan
London
nan
nan
Beijing, China
San Diego, CA
London
Tour Bus or Airplane/ Hotel
nan
nan
Central NJ
Fredericksburg, VA
Central NJ
San Diego, CA
FL
nan
Irving Texas
nan
nan
nan
Portsmouth, NH
Indianapolis, IN
Bellaire TX
nan
Portsmouth, NH
nan
New York City
Oklahoma
nan
Alexandria, VA
Rhode Island
Bechtelsville, PA
AustinTX-AUS/CaliColombia-CLO
Atlanta, GA
Atlanta, GA
iPhone: 55.946030,-3.189382
Atlanta, GA
Atlanta, GA
nan
nan
nan
nan
Houston, TX
FL
New York City
43206
Fredericksburg, VA
Medway, MA
nan
Columbus, Ohio, USA
nan
Oklahoma
nan
Winnipeg, Manitoba
nan
wdc
Winnipeg, Manitoba
Winnipeg, Manitoba
New York, NY
Fredericksburg, VA
nan
california
Chicago
california
Edinburgh
nan
Chicago
Kansas City, USA
nan
Happy Valley
nan
nan
201-600-2130
Chicago
nan
nan
South Seaside Park/Puerto Rico
nan
Oxford, MS
nan
nan
nan
nan
New York City
43206
nan
nan
nan
nan
nan
nan
nan
nan
nan
Rio Grande Valley, Texas
the burgh
Grimsby - UK
London
San Diego, CA
New York City
SE AZ By Way of HV
fort worth, tx
Chicago
Williamsburg, Brooklyn
nan
Irving Texas
Rochester
Colorado
SE AZ By Way of HV
nan
Houston, TX
whore island
nan
Hong Kong
nan
the burgh
nan
Virginia, USA
nan
nan
Huntington, WV
PDX
Atlantic Highlands, NJ
PDX
nan
Northern Virginia
Huntington, WV
nan
the district of colombia
Metro DC Area
ÜT: 43.492878,-96.790916
Curitiba
Kingston, Ontario
nan
senior / mount airy
nan
Akron, Ohio
Atlantic Highlands, NJ
Seattle WA
Akron, Ohio
Atlantic Highlands, NJ
Akron, Ohio
nan
Atlantic Highlands, NJ
Atlantic Highlands, NJ
nan
Bedford 2015 Plymouth 2019
nan
Washington, DC
Belgium
WDVE PITTSBURGH
Rio Grande Valley, Texas
nan
nan
nan
san diego
Raleigh, North Carolina, USA
London
Akron, Ohio
nan
Guatemala
PDX
Terra Prime
nan
#Westford #marketing
nan
nan
nan
Long Island, NY
Boston, MA
nan
nan
nan
nan
nan
nan
Boston, MA
Bedford, Nh
Bedford, Nh
nan
nan
nan
nan
nan
nan
nan
Berlin, Germany
nan
nan
San Francisco
Honolulu, HI
Raleigh, NC
Southern California and Hawaii
sf - austin - sydney
Little Rhody
iPhone: 55.946030,-3.189382
nan
East Coast
Potsdam, NY
Oklahoma
Yokohama, Japan
☁️
Yokohama, Japan
Blue Area of the Moon
New York City
Washington, DC.
Kansas
Blue Area of the Moon
Blue Area of the Moon
☁️
☁️
Blue Area of the Moon
nan
Kalispell, MT
Boston
Vancouver, BC
Brookline, Massachusetts
East Coast
nan
nan
New Haven, CT
Branford, CT
Central NJ
USA
nan
nan
Wake Forest, NC
Wake Forest, NC
Wake Forest, NC
Wake Forest, NC
Wake Forest, NC
Belmar, NJ
Wake Forest, NC
nan
Fredericksburg, VA
Wake Forest, NC
Wake Forest, NC
Venice, CA
Little Rhody
somerville nj
Boston, MA
Little Rhody
Boston, MA
Chicago, IL
somerville nj
Little Rhody
nan
Kaizen 改善
Hoboken, NJ
nan
sf - austin - sydney
nan
San Francisco, CA
San Francisco, CA
San Francisco, CA
Little Rhody
nan
Toronto, Canada
Htown
New York
San Francisco
just living the dream
nan
Greater Seattle Area
Vancouver, Canada
nan
Montana, God's country
Bedford, Nh
Washington-Vancouver-Medellín
nan
nan
#Westford #marketing
#Westford #marketing
nan
Montreal
Washington-Vancouver-Medellín
New York, NY
Oxford, MS
Boston, MA
Boston, MA
Boston, MA
Washington-Vancouver-Medellín
Boston, MA
nan
Ottawa, ON
Ottawa, ON
PDX / LGA / TXL / ✈
nan
nan
nan
Global
Sweden
New Haven, CT
nan
San Diego, CA
Raleigh, NC
Rhode Island
arlington, va
San Diego, CA
Chicago, IL
Coastal
nan
Coastal
ORLANDO, FL
#Westford #marketing
Rhode Island
Rhode Island
Cheltenham
Rhode Island
Rhode Island
Austin, TX
Austin, TX
nan
401 SK
nan
Belfast/Boston
nan
Boston, MA
Saskatchewan
nan
nan
#Westford #marketing
Blue Area of the Moon
Ottawa, Canada
Cambridge, MA, USA
nan
ORLANDO, FL
Chicago
nan
Boston, MA
Wilmington, Delaware
San Francisco, California
Vancouver, WA
Chicago, IL
nan
Ocean, NJ
Philly Burbs
nan
Columbus,Ohio
Columbus,Ohio
nan
nan
Washington, DC
Austin, Tx
Milwaukee, WI
Vernon Hills, IL
nan
Chicago, IL
Milwaukee, WI
New Haven, CT
Milwaukee, WI
nan
toronto
pensacola
Brooklyn, NY
nan
Ottawa, ON
nan
nan
Global
Montreal, Quebec CA
Global
nan
nan
Hoboken, NJ
NYC
San Francisco, CA
nan
Oxford, MS
Oxford, MS
San Francisco, CA
Cambridge, MA, USA
nan
New Haven, CT
nan
New Haven, CT
New York, NY
Sydney, Australia
Oxford, MS
Montana, God's country
toronto
nan
Foster City, CA
Buffalo, NY
Boise
Blue Area of the Moon
Central NJ
nan
Washington, DC
san diego
Portland, Oregon
Beijing | Los Angeles
Heart of America
nan
Pittsburgh
Blue Area of the Moon
nan
☁️
New Haven, CT
Blue Area of the Moon
nan
Chicago, IL
Hoboken, NJ
Boston, MA
Hoboken, NJ
nan
A Californian in London
New York City
Boston, MA
A Californian in London
Virginia/Metro Washington, DC
San Francisco, CA
Mexico City, Mexico
Muswell Hill
Wonderland
What day is it?
San Francisco, CA
Wonderland
Chicago, IL
South Australia
nan
Austin, TX
Philly Burbs
Grimsby - UK
Pittsburgh
Raleigh, NC
California
Pittsburgh
Gooner
nan
iPhone: 0.000000,0.000000
A Californian in London
nan
nan
nan
San Francisco, CA
Seattle, WA / 36,000 feet
Mexico City, Mexico
nan
nan
USA
Dallas, TX
USA
midwest and sometimes Spain
midwest and sometimes Spain
nan
Boston MA
nan
nan
nan
nan
nan
nan
nan
nan
just living the dream
nan
just living the dream
Minnesota, USA
GORGEOUS UNIVERSE
The Garden State
Bethlehem, PA
England
Chicago
nan
nan
Raleigh, NC
nan
nan
nan
midwest and sometimes Spain
nan
Fresno, CA
One of the C-gates at EWR.
nan
Raleigh, NC
Raleigh, NC
just living the dream
nan
Global
Madison, WI
Washington, DC
Grand Rapids, Michigan
Bethlehem, PA
nan
31.790466, -85.971558
nan
nan
The Garden State
nan
England
nan
nan
Seattle WA
Seattle WA
New York City
America
nan
SoCal
Seattle WA
Seattle WA
nan
One of the C-gates at EWR.
San Francisco
Rochester NY
nan
Houston, TX
nan
Chicago, IL
nan
new york, baby
Central Sq. Cambridge, MA
Forest Grove, OR
31.790466, -85.971558
nan
Forest Grove, OR
Vancouver, BC
nan
nan
San Diego
nan
nan
Richmond, VA
Silicon Valley, CA
midwest and sometimes Spain
Washington, DC
ÜT: 39.768182,-86.167261
washington dc
Portland, Oregon
nan
Pittsburgh
Houston, TX
SoCal
nan
nan
nan
nan
nan
nan
Beautiful Stockton, CA
Washington, DC
Beautiful Stockton, CA
Beautiful Stockton, CA
Atlanta, GA & St Augustine, FL
Atlanta, GA & St Augustine, FL
Chicago, IL
Ashburn, VA
Kingston, Canada
nan
Middlebury, VT
Middlebury, VT
Beautiful Stockton, CA
Rixensart, Belgium
NOLA Reb
Boston, MA
Brooklyn, NY
Chicago, IL
nyc
Pursuit of Happiness
NOLA Reb
nan
NJ
nan
Chicago, IL
Toronto, Canada
nan
NYC & SEAsia
nan
Gig Harbor
nan
• Lacrosse ||'17|| Football •
nan
nan
nan
San Francisco
nan
nan
nan
Atlanta
ONTARIO CANADA
nan
nan
nan
Chicago, IL
Montreal, Quebec CA
nan
Salt Lake City, UT
Boise
Calgary, Canada
Colorado
nan
Atlanta
Apex, NC
SoMD
ONTARIO CANADA
nan
Ontario, Canada
Aliso Viejo, CA
Winnipeg, Manitoba
nan
Cleveland, Ohio
nan
Montreal
nan
Enfield, CT
nan
Washington, D.C.
Canton Mass
Ohio
sf - austin - sydney
iPhone: 50.079998,14.441111
Here, There or En Route,
nan
New York
UK
In First Class
New York
nan
Rochester NY
nan
Boston, MA
Chicago
nan
nan
nan
New York, NY
nan
YYC
nan
nan
Denver, CO
Upper East Side
nyc
nan
nan
State College, PA
Boston, MA
San Francisco
nan
nan
Springfield, MO
nan
nan
nan
nan
nan
Boston, MA
Cloud City, MA
nan
nan
nan
Manassas, Virginia
YYC
here
nan
nan
New Jersey, USA
Nashville, TN +309+ +320+
NY
Denver, CO
ÜT: 39.39339,-76.762595
nan
nan
nan
nan
nan
ÜT: 42.706796,-71.210754
Houston, TX
nan
nan
Little Rock
Norman, OK
Park City, UT
Raleigh, NC
Raleigh, NC
nan
Chicago
Lake City, MN
Chicago
Chicago
Colorado
Colorado
Houston
Amsterdam/Malibu
New York, US; Tanzania, Africa
iPhone: 0.000000,0.000000
Colorado
Colorado
Colorado
Colorado
Orleans/Tarpon Springs/London
Shanghai, China
nan
nan
London-Manila
Chicago Illinois Crime.Inc
Global
Tulsa, OK
Email: Alex@ditmasla.com
Chicago Illinois Crime.Inc
Coming to a City Near You
Chicago Illinois Crime.Inc
Coming to a City Near You
Massachusetts
Boston, MA
Chicago, IL
nan
Edinburgh
Cleveland, OH
nan
Kearney, Nebraska
Chicago, IL
chicago IL
Albuquerque, NM
Chicago, IL
Aliso Viejo, CA
ridin' da food train
Kearney, Nebraska
Boulder, CO
Hong Kong
Kearney, Nebraska
Hong Kong
Austin
Cincinnati
nan
Cincinnati
Hong Kong
Hong Kong
Chicago, IL
nan
Southern California and Hawaii
nan
Hong Kong
Cincinnati
NYC
Vancouver, Canada
nan
Grand Rapids, Michigan
Redmond WA
Omaha, Nebraska
Washington
nan
Anywhere someone needs me
Anywhere someone needs me
Plymouth, IN
chicago IL
Vail, CO
chicago IL
chicago IL
Wisconsin to Worldwide
nan
nan
nan
O H I O
ONTARIO CANADA
nan
nan
Gig Harbor
The abyss
Ho-Flo/Columbus/NYC
Exactly where I want to be!
San Diego
Denver, CO
nan
Hoboken, NJ
O H I O
nan
Ho-Flo/Columbus/NYC
With Carmen SanDiego.
Lea Michele
Fort Lauderdale, FL
Omaha, Nebraska
Bellingham, WA
nan
nan
Vancouver, Canada
DFW area texas
Hoboken, NJ
nan
Obersulzbach, Germany
nan
nan
nan
nan
nan
Edmonton, Alberta, Canada
Silver Spring
Silver Spring
Vail, CO
Vail, CO
Silver Spring
nan
Location: constantly changing!
TRACK
nan
Ho-Flo/Columbus/NYC
Cleveland, OH
nan
Someplace saving something
San Diego
Rutgers Business School - NB
Crowborough
Washington DC
San Diego, CA
Washington
Hilton Head, SC
Gig Harbor
San Francisco
New York City
nan
nan
Omaha, Nebraska
Hoboken, NJ
nan
Chicago
Elmira, NY
NY & NJ
nan
nan
Boston, Massachusetts
Fort Lauderdale, FL
Hoboken, NJ
nan
Washington DC
Boulder, CO | Los Angeles, CA
Evanston, IL
Brooklyn, NY
Chicago
nan
In & Around Chicago
West Virginia
Buck-hio
Chapel Hill
New York City
Vail, CO
nan
Minneapolis
sf - austin - sydney
Omaha, NE
Winnipeg, MB Canada
Winnipeg, MB Canada
Silver Spring
nan
Boulder, CO | Los Angeles, CA
washington, dc
washington, dc
nan
Los Angeles, CA (via Philly)
San Francisco
Denver
United States
nan
nan
nan
nan
Apex, NC
Elmira, NY
nan
nan
nan
Austin, Texas
nan
New York, NY
nan
NY
nan
chicago
New York | Nomad
New York City
New York
Los Angeles, CA (via Philly)
Grand Junction, Colorado
Boulder, CO
Los Angeles, CA (via Philly)
MN, MI, NM
Kenosha, WI
Clifton, NJ
ÜT: 38.763515,-77.718226
Brooklyn, NY
NY
nan
Austin, Texas
New York, NY
New York, NY
Winnipeg, MB Canada
New York
New York
Denver, CO
New York, NY
nan
Evanston, IL
Evanston, IL
Los Angeles, CA
Greater New York City Area
Boston
nan
the nation's capital
Radford HS
Sacramento, CA
nan
washington, dc
Powell, OH
nan
#avgeek, United 1K
Culver City, CA
Vail, CO
San Jose, CA
Oakland
Los Angeles
nan
San Francisco, CA
Denver
Hong Kong
Los Angeles
Culver City, CA
nan
nan
washington, dc
Washington, DC
Parkersburg, WV
Los Angeles
Los Angeles
nan
nan
Purcellville, VA
Purcellville, VA
Kenosha, WI
Austin, TX
Austin, TX
Someplace saving something
San Jose, CA
Purcellville, VA
Purcellville, VA
New York
Purcellville, VA
Deutschland
Parkersburg, WV
Aberdeen
San Diego, CA
Purcellville, VA
nan
San Diego, CA
Chicago
Salt Lake City, UT
Edinbrah
Edinbrah
USA
Kenosha, WI
Southern California
Global
nan
Edinbrah
Edinbrah
Edinbrah
Salt Lake City, UT
Salt Lake City, UT
San Francisco, CA
Salt Lake City, UT
USA
Montana
Baltimore, MD
Baltimore, MD
NY
Alphen aan den Rijn
Baltimore, MD
Baltimore, MD
Baltimore, MD
San Francisco
Los Angeles
New Jersey
New York State
bonkers in Yonkers
Chicago
nan
New York State
Baltimore, MD
nan
Midwest
Sacramento, CA
Brighton, UK
Atlanta, GA
nan
Massachusetts
USA
nan
nan
San Francisco
nan
Charleston, SC
Portland
nan
Corrupt California
Cleveland, OH
Being not the one
New York
nan
San Francisco
nan
Someplace saving something
New York, NY
Washington, DC
Tri State NY area...
Atlanta, GA
Baltimore, MD
New Jersey
Baltimore, MD
The Gaming Cartel
Baltimore, MD
Baltimore, MD
nan
nan
nan
Santa Barbara CA
nan
USA
D.C.
Chicago & Worldwide
YHZ
Tri State NY area...
The Gaming Cartel
South Texas
Celebration, FL
Celebration, FL
Toronto, ON
Austin, Texas
Global
Minneapolis, MN
nan
Chicago
Red Bank, NJ
nan
D.C.
nan
PDX / LGA / TXL / ✈
nan
Chicago
nan
Atlanta, GA
PDX / LGA / TXL / ✈
Atlanta, GA
Mammoth Lakes
Boston, MA
New Jersey
nan
Celebration, FL
The Commonwealth of Virginia
nan
Chew Valley, North Somerset UK
nan
Newark, NJ
Brighton, UK
NY
nan
Louisville, KY
Atlanta, GA
Louisville, KY
Bay Area
nan
Benice Veach, CA
nan
nan
Global
NY
nan
NY
Studio City, CA
NY
South Texas
nan
nan
Red Bank, NJ
Denver
Calgary
nan
Easley SC
YHZ
nan
nan
San Francisco
nan
nan
San Francisco
Columbia, MO
Texas, duh!
Louisville, KY
Louisville, KY
NYC
Louisville, KY
Tetbury
Alphen aan den Rijn
nan
nan
nan
nan
nan
nan
Manassas VA
Columbia, MO
nan
nan
Easley SC
ÜT: 40.635407,-73.991869
nan
New York City, baby
nan
Global
Washington, DC - USA
nan
New York, New York
nan
Off: NYC, Home: Hoboken, NJ
Columbus, Ohio
nan
Louisville, KY
Portland, OR
nan
Obersulzbach, Germany
Chicago, IL
Columbus, Ohio
Toronto
San Francisco, CA
nan
Mexico
San Francisco, CA
Off: NYC, Home: Hoboken, NJ
Global
Kalamazoo & Chicago
San Francisco
nan
Denver
nan
nan
nan
Chicago, IL
Tri State NY area...
Elmira, NY
Ottawa, ON
nan
Northern Ireland
New York, NY
Orlando, FL
Orlando, FL
san francisco
Obersulzbach, Germany
Ottawa, ON
nan
nan
In First Class
Tri State NY area...
Orlando, FL
Ohio (by way of Nebraska)
Elmira, NY
New York, Ny
Global
Elmira, NY
Boston, MA
NYC
NYC
Red Bank, NJ
NYC
NYC
Boston, Massachusetts
Lehigh Valley, PA
Alphen aan den Rijn
nan
Tri State NY area...
las vegas/nashville, TN
Minnesota, USA
Chicago, IL
Ottawa, ON
nan
Phila. PA
nan
New Jersey
London, UK
nan
nan
Abu Dhabi
Off: NYC, Home: Hoboken, NJ
nan
Chicago, IL
NYC. Sports.
Alphen aan den Rijn
Los Angeles/Charlotte
FUCK CANCER
Naperville
houston paris nyc
New York
Charlottesville, VA
Seattle
New Jersey
New York, NY
38.04591,23.769275
Orlando, FL
Charlottesville, VA
New Jersey
nan
Washington, DC - USA
nan
santa barbara, CA
minneapolis
New Jersey
NY
New York
nan
Chicago, IL
nan
Warsaw, Berlin, Kiev
nan
Portland, OR
New York
London via Adelaide
Tulsa, OK
nan
nan
nan
nan
nan
Birkenhead upon Hudson
Los Angeles, CA
nan
nan
Bellevue Washington
bonkers in Yonkers
bonkers in Yonkers
bonkers in Yonkers
nan
nan
bonkers in Yonkers
bonkers in Yonkers
nan
nan
nan
Seattle, WA
nan
Houston
SoCal is where my mind stays
Houston
San Francisco, Ca
Kaizen 改善
NJ
Yonkers NY
fuckin portland ass oregon
nan
Houston
Kaizen 改善
New Jersey
San Francisco, CA
Houston
DC/SF/BFLO
nan
Chicago
SoCal is where my mind stays
nan
Los Angeles, California
Los Angeles, CA
nan
nan
Alphen aan den Rijn
Los Angeles, California
nan
nan
Vancouver, BC
San Francisco, CA
nan
UK
nan
nan
Los Angeles, CA
Arlington Virginia
IL
nan
Los Angeles, California
nan
Boston, MA
Los Angeles, California
nan
Pursuit of Happiness
Nanjing, China
IL
nan
Sacramento, CA
New York, NY
St. Louis, MO
Chicago, IL
nan
nan
nan
Los Angeles, CA
Raleigh-Durham, NC USA
nan
nan
Boston, MA
Raleigh
nan
Chicago, Illinois.
Chicago, Illinois.
Boston, MA
nan
Chicago, Illinois.
ÜT: 40.635407,-73.991869
Ottawa/St.Louis
Ottawa/St.Louis
Houston, Texas
nan
nan
nan
nan
Chicago, Illinois.
nan
Arlington, VA
nan
St. Louis, MO
nan
New York
New York
Houston
nan
nan
nan
Where the miles are
Buffalo, NY
Indy
LA / NYC
DC/SF/BFLO
nan
nan
nan
DC/SF/BFLO
DC/SF/BFLO
nan
nan
Allentown, PA
sunnyvale, ca
Omaha, NE
nan
nan
nan
Louisville, KY.
nan
Carmel, IN
Cleveland
nan
nan
Houston
ÜT: 38.988512,-77.161145
nan
nan
San Jose, CA
Los Angeles
Pursuit of Happiness
New York | Nomad
nan
Arlington, VA
PA
Pursuit of Happiness
Minnetonka
Pursuit of Happiness
Las Vegas & Atlanta
nyc
Pursuit of Happiness
Jacksonville Beach, FL
nan
nan
nan
Hoboken, NJ
San Francisco
Jacksonville Beach, FL
nan
nan
Jacksonville Beach, FL
nan
nan
New York
nan
nan
Chicago
nan
nan
Chicago
Everywhere!
nan
nan
Columbus, Ohio
Nicaragua
lexington KY
NY
lexington KY
nan
Carmel, IN
Pursuit of Happiness
Brooklyn
nan
Chicago
Dallas, TX
nan
nan
Chicago, Illinois
htx
nan
Irvine, CA
nan
nan
San Francisco, CA
USA
nan
Dallas, TX
nan
nan
San Francisco, CA
Houston, TX, USA
Pursuit of Happiness
Pursuit of Happiness
San Francisco, CA
Texan in Los Angeles
Detroit, Michigan
Somerville, MA
Madison, wi
nan
Canmore
BTR/DCA/IAD/MSY - etc
San Juan Capistrano
iPhone: 40.732048,-73.994102
San Francisco, CA
America's Dairyland
nan
nan
nan
Tulsa, OK
nan
Las Vegas & Atlanta
Las Vegas & Atlanta
Look behind you
nan
nan
Madison, wi
nan
nan
nan
Look behind you
San Diego, CA
New York, NY
Tulsa, OK
nan
860
Somewhere west of Chicago
nan
Tulsa, OK
BOS, SFO, NYC, ++
nan
America's Dairyland
PA
nan
Silver Spring,MD
Alphen aan den Rijn
New York
nan
nan
nan
Tulsa, OK
Brighton, UK
Brazil
Hudson Valley, NY
Madison, wi
Chicago and an airplane
Someplace saving something
Tulsa, OK
Mysore : London : New York
Brighton, UK
Arlington Virginia
bettendorf, ia
nan
nan
Oak Park, IL
Fort Wayne
Monett, MO
nan
nan
Fort Wayne
Dallas/Eugene
Birkenhead upon Hudson
San Diego
nan
iPhone: 40.732048,-73.994102
nan
nan
nan
nan
nan
Columbus, Ohio
iPhone: 40.732048,-73.994102
Canmore
I'm just a kid from Oak Harbor
Alphen aan den Rijn
nan
nan
Greater New York Area
Bushwick, Bkln
nan
Portland, OR
nan
nan
PA
ny
Someplace saving something
nan
PA
nan
new york city
nan
nan
nan
nan
PA
iPhone: 41.871374,-72.497792
iPhone: 40.773155,-73.872490
Los Angeles
nan
usa
nan
Tulsa, OK
nan
nan
nan
Denver Colorado
Brooklyn
São Paulo / Brasil
nan
São Paulo / Brasil
Almere, The Netherlands
nan
LA | SF | Bicycle | 38,000'
North Jersey
nan
Walnut Creek, CA when I'm ther
France
East&West Coasts & the South
nan
Costa Mesa, CA
North Jersey
New York City
Chicago, Il
Chicago, IL
On a couch, with a computer.
nan
Germany
nan
nan
nan
nan
nan
North Jersey
nan
Los Angeles, CA
Los Angeles, CA
Near Washington, DC
Texas
New York, NY
nan
PA
PA
Houston, TX, USA
Ventura, California
nan
Washington DC Metro Area
LA | SF | Bicycle | 38,000'
ny
Winchester, MA
San Francisco, CA
Long Island, NY
Chicago, IL
New York, NY
Chicago, IL
Portland, OR
Houston, Tx & Old Bridge, NJ
Connecticut
nan
nan
nan
nan
nan
Los Angeles, CA
nan
nan
New York
Connecticut
New York City (Silicon Alley)
nan
Bangalore
Luxembourg
Luxembourg
New York
nan
Bangalore
nan
nan
nan
New York, NY
Austin, TX
California
Germany
Wisconsin, ya know
raleigh, nc
Rosenberg, Texas
New York, NY
Wisconsin, ya know
Brooklyn, NY
nan
nan
CT • MINDLESS EP ON SALE! ⬇️
Jersey City, NJ
nan
Ratner Companies, Hair Cuttery
Jerusalem-London-Antwerp
Paris
New York
NJ/NYC
Paris
iPhone: 40.773155,-73.872490
Germany
nan
Monterrey
iPhone: 40.773155,-73.872490
nan
Amsterdam
nan
Boise, Idaho
nan
Lexington, Kentucky
nan
Rhode Island
nan
Germany
nan
Rhode Island
shanghai, china.
nan
Rhode Island
Los Angeles
Rhode Island
Rhode Island
Rhode Island
Rhode Island
SYD▫️
Rhode Island
New York City (Silicon Alley)
Chicago, IL
Chicago, IL
nan
London Heathrow.
Houston, TX
Houston, TX
Chicago Illinois Crime.Inc
ÜT: 37.748534,127.066482
nan
nan
nan
Keauhou, Hawaii USA
nan
Austin, TX
Austin, TX
nan
nan
nan
Houston, Texas
Washington D.C.
roblox.com
Keauhou, Hawaii USA
New York
Singapore
New York
Houston, TX
nan
Washington D.C.
Washington D.C.
nan
Houston, TX
CT • MINDLESS EP ON SALE! ⬇️
NYC - DC - LA
Chicago, Illinois.
nan
nan
nan
Rhode Island
San Francisco
Sydney Australia
Washington, DC
CLE
nan
Detroit, Michigan
nan
nan
Panamá
nan
Mill Valley, CA
Mill Valley, CA
Colombia
nan
nan
Seattle
Seattle
San Francisco, CA
Philadelphia, PA
Houston, TX
Fredericton NB
Fredericton NB
Houston, TX
Seattle
nan
Houston, TX
Princeton, NJ
Houston, TX
Seattle
Columbia, MO
ny
Seattle
nan
East Hanover, NJ
Fredericton NB
ny
nan
Brisbane, Australiaaa!
Brisbane, Australiaaa!
Rozelle, NSW
nan
nan
Buffalo, Ny
Austin, TX
Sydney
roblox.com
San Francisco, CA
nan
Houston, TX
Sydney, Australia
Dallas, TX
nan
Fredericton NB
Chicago, IL
ny
nan
D.C.ish
nan
Semiahmoo, WA Soonerland
Q's, Staples Center
Chicagoland
ÜT: 38.006902,-78.502005
San Francisco, California
Dodging Traffic
Curitiba
Chicago, IL
nan
California, US
New York
nan
nan
EIN: 27-0575748 -New York -USA
nan
nan
Rhode Island
Port Townsend, WA
Chicago, IL
nan
New York City
Englewood, Florida
NC
Staten Island, NY
nan
St. Louis, MO for now
Boston, MA
London
Barcelona
Bend, OR
Elkhart, Indiana
Wherever.
Ratner Companies, Hair Cuttery
nan
Los Angeles, California
Memphis TN
London
nan
ny
Staten Island, NY
I'm just a kid from Oak Harbor
Tweets = My Opinion
nan
nan
Las Vegas
Cardiff and London
London
New York, NY
Sun Devil Territory
Franklin, Tennessee
BTR/DCA/IAD/MSY - etc
San Francisco, CA
I'm just a kid from Oak Harbor
Elkhart, Indiana
roblox.com
Elkhart, Indiana
nan
nan
San Francisco, California
nan
nan
BTR/DCA/IAD/MSY - etc
Panamá
Virginia
Brooklyn, NY
San Francisco
Oahu, Hi
nan
Seattle
nan
Colombia
nan
nan
nan
Dallas, TX
nan
nan
nan
nan
CO Springs, Occupied Colorado
nan
Lakewood, Colorado
Memphis TN
Houston, Texas
Boston, MA
nan
Colombia
Boston, MA
NJ
NJ
NJ
NJ
NJ
nan
NJ
NJ
Earth (for now...)
california
NJ
Chicago, Illinois
nan
nan
roblox.com
Texas, USA
California, US
Houston, Texas
nan
Texas, USA
ÜT: 33.449738,-112.049072
nan
Arlington, VA
Chasing Carmen San Diego
Washington, DC
NJ
nan
Jacksonville, FL
Texas, USA
Brisbane, Australiaaa!
Mobile, AL.
nan
minneapolis
nan
Paramus, NJ
DFW & SF
Fredericton NB
San Francisco Bay Area, USA
Sacramento, CA / Columbus, OH
Texas, USA
San Francisco Bay Area, USA
nan
Born/Raised in 314/Home is 317
Texas, USA
London
columbus, oh
Houston TX
New York, NY
nan
San Francisco
Fremont, California
United States
nan
nan
London
USA
nan
nan
Oahu, Hi
Novi, MI
columbus, oh
Denver, CO
nan
Jersey City, NJ
new york city
Edmonton, Alberta, Canada
San Francisco
London
DC
nan
nan
DFW & SF
San Francisco Bay Area, USA
nan
nan
United States
Jersey City, NJ
nan
São Paulo / Brasil
Jersey City, NJ
nan
Las Vegas
nan
nan
columbus, oh
columbus, oh
nan
DC/SF/BFLO
Chasing Carmen San Diego
West Wickham Kent !
Nicaragua
Instagram @thejetsetjulie
Belle Mead, NJ
San Diego
LA, NY, Chicago & St.Louis
nan
LA, NY, Chicago & St.Louis
Houston TX
nan
nan
Austin, TX
New York City (Silicon Alley)
Amsterdam NL
nan
nan
california
San Diego
Raleigh, NC SoCal
San Francisco, CA
Grand Junction, CO
nan
Fremont, California
Fremont, California
nan
NYC
NYC
Arlington, VA
NYC
columbus, oh
on my iPhone
D.C.ish
columbus, oh
columbus, oh
District of Columbia
Orlando, Florida
Chasing Carmen San Diego
New York, NY
nan
Orlando, Florida
nan
bonkers in Yonkers
nan
Canada
Chicago
San Diego
Wrigley, California ☀
Illinois
Canada
Amsterdam
nan
Brisbane
nan
Washington, DC
New Canaan, Ct
Canada
New Canaan, Ct
Austin, TX
Salt Lake City, UT
NYC
Fremont, California
Warsaw
LA, NY, Chicago & St.Louis
Curitiba
Mill Creek HS
Abu Dhabi
Minneapolis, MN
Needham
Mexico City
nan
Salt Lake City, Utah
Boise, ID
NA, SA, Europe
Los Angeles & Vancouver
nan
nan
New York, NY
DC
nan
Founder @catapultgrp @ilikeoi
Needham
Raleigh, North Carolina
nan
All Over The World
Bergen, Norway
nan
nan
nan
New York
nan
Boise, ID
Wellesley, MA
New York, NY
columbus, oh
Chicago
Ottawa, Canada
Ottawa, Canada
Ottawa, Canada
Fremont, California
Washington DC
Canmore
Ottawa, Canada
New York, NY
Meridian ID
Littleton, CO
nan
nan
Denver, CO
Wherever the Army sends me
nan
nan
nan
Brentwood, CA
nan
nan
New York, New York
Gdansk / Manchester
Alvechurch, Worcestershire, UK
London, UK
Redhill, UK
nan
bonkers in Yonkers
bonkers in Yonkers
bonkers in Yonkers
bonkers in Yonkers
wishing i was in Vernon
Gdansk / Manchester
Toronto
Toronto
planet earth
Toronto
Toronto
Flightlever 340
London, UK
nan
nan
nan
All Over The World
New Canaan, Ct
North West of England
North West of England
nan
Wherever the Army sends me
nan
nan
90210
All Over The World
Montclair, NJ
Ventura, California
NYC Area
San Diego, CA by way of VA
Northam Stand, Southampton
All Over The World
San Francisco, CA
NYC
Brisbane
nan
All Over The World
nan
nan
nan
Sarasota, Florida
So Cal
So Cal
All Over The World
All Over The World
Brooklyn, New York
Salt Lake City, Utah
nan
TEXAS
nan
Brooklyn, NY
nan
NA, SA, Europe
NA, SA, Europe
Nicaragua
the questions district
Toronto
Washington DC
San Francisco, CA
A Texan in Toronto
Nashville, TN
Nashville, TN
All Over The World
All Over The World
Costa Mesa, CA
nan
Bellingham, WA
Vancouver, Canada
OAK
New York City
nan
New York City
New Orleans
nan
Canmore
nan
nan
San Francisco
Boise, ID
San Francisco
Boise, ID
NYC
nan
nan
New York, NY
Washington DC
San Francisco
nan
Irvine
Irvine
nan
Washington DC
Washington DC
nan
SLC | LA
Irvine
Irvine
Irvine
Irvine
Irvine
des moines
nan
nan
SLC | LA
SLC | LA
nan
nan
nan
nan
nan
Canmore
Virginia Beach, VA
Kansas City
nan
Irvine
Washington DC
Chicago, IL
nan
Sarasota, Florida
Sarasota, Florida
Irvine
Irvine
Irvine
Irvine
nan
Los Angeles, CA and the world.
nan
PDX / LGA / TXL / ✈
Pittsburgh
Space City, Hong Kong, NOLA
nan
OAK
Hilo, HI
Napa
└A
nan
California
New York
nan
nan
toronto
Amsterdam or 30,000 Feet
nan
Arlington, Texas
Johnstown, Ohio
nan
nan
Memphis
NYC
nan
nan
Hampton Roads, Virginia
Chicago, IL
nan
nan
nan
Canmore
Washington DC
Berkeley, CA
Canmore
Usually in a plane
Flightlever 340
San Francisco, CA
Los Angeles
nan
San Francisco, CA
San Antonio
Wichita, KS
California
San Francisco, CA
Washington DC
San Francisco, CA
nan
nan
nan
Usually in a plane
toronto
toronto
nan
✈
California
nan
California
Canmore
Ventura, California
San Antonio
nan
nan
nan
nan
Hilo, HI
nan
Crowley, Louisiana
Hilo, HI
nan
nan
North West of England
nan
nan
Hampton Roads, Virginia
The Netherlands
The Netherlands
nan
nan
san diego
US
the questions district
Canmore
North West of England
between Warsaw and London
North West of England
North West of England
The Netherlands
nan
nan
NYC, Toronto
Conway, AR
Costa Mesa
Baton Rouge, LA
New Canaan, Ct
San Bruno, CA
Ontario, Canada
Baton Rouge, LA
London, UK
nan
The Netherlands
All Over The World
Nova Scotia
Southern New Jersey
nan
Germantown, MD
nan
San Francisco
Loving the Languageof Literacy
Ontario, Canada
Denver, CO
New York, NY
Chicago, IL
Ontario, Canada
Ontario, Canada
Sacramento
The Netherlands
Conway, AR
nan
Nederland
Southern New Jersey
North Hollywood, CA
san diego
nan
nan
New Canaan, Ct
NJ
So Cal
nan
Washington, DC
Loving the Languageof Literacy
Titletown, USA
North West of England
All Over The World
Washington DC
NYC, Toronto
New York, NY
chicago, il
Costa Mesa
Ravens Nation 51814
Denver
Washington, DC
Conway, AR
Costa Mesa, CA
The Netherlands
NoVa
Glasgow
Brentwood, CA
Brentwood, CA
Brentwood, CA
chicago IL
New Canaan, Ct
NYC, Toronto
The Netherlands
Washington, DC
Italy
nan
Loving the Languageof Literacy
St. Pete
West Coast of America
nan
New York | Los Angeles
North Plainfield, NJ
nan
Colorado
Italy
New York, NY
Jerusalem-London-Antwerp
Chicago
nan
All Over The World
Memphis
LA
nan
Ontario, Canada
Washington, DC
Memphis
Port Washington, NY 11050
Memphis
North Tonawanda
St. Pete
Memphis
NYC
Memphis
Memphis
The Lone Star State
The Lone Star State
NYC
nan
North West of England
nan
Bozeman, Montana
nan
Usually in a plane
nan
nan
Denver, CO
North Hollywood, CA
nan
nan
nan
nan
nan
Plattsburgh, New York
fairfield county, connecticut
nan
Colorado
nan
NJ/NYC
nan
Northwestern NJ
nan
ÜT: 40.635407,-73.991869
Toronto
Northwestern NJ
Port Washington, NY 11050
Washington, DC
Southern New Jersey
nan
Houston
Massachusetts
Houston, Texas
nan
nan
nan
nan
ottawa
All Over The World
nan
All Over The World
NYC, Toronto
Halifax, Nova Scotia
nan
pittsburgh
nan
Deerfield
Indianapolis
nan
nan
Bhutan
Usually in a plane
Bhutan
Bhutan
nan
nan
Bhutan
Kanasa City
Bhutan
So Cal
Salt Lake City, Utah
Bhutan
Bhutan
Bhutan
New York, NY
nan
Bhutan
Nova Scotia
Toronto
West Palm Beach
nan
North West of England
Island of Oahu, Hawaii
Los Gatos, CA
Jerusalem-London-Antwerp
Tucson, AZ and Vancouver, BC
Bhutan
Virginia
Boston, MA
Wheatridge,Colo
Houston
ÜT: 40.976702,-72.210688
Portsmouth, UK
Glasgow
grand rapids, michigan
ÜT: 34.188976,-118.613497
Houston
New York, NY
Omagh/Belfast
nan
nan
nan
Washington, DC
New Jersey
Tweets = My Opinion
Portland, OR
nan
nan
Washington, DC
Today I'm in: Maryland
ÜT: 38.965477,-77.428287
nan
nan
nan
St. Louis, MO
Chappaqua NY
Chappaqua NY
Columbus, OH
nan
nan
New York, NY
Chicago, IL
TX
Burlington, Ma
Chappaqua NY
nan
nan
San Antonio, TX
West Palm Beach, FL
St.Peters, MO
lurking in a coffeehouse
Orange County
1/1 loner squad
nan
New York, NY
Eau Claire
Chappaqua NY
Denver, CO
Trapped in Baltimore.
nan
Fort Lauderdale
Fort Lauderdale
EVERYWHERE
Fort Lauderdale
nan
nan
Chicago
Atlanta, GA
Grandville, Michigan
Around...
603 | 205
nan
woodstock, Ontario, Canada
lurking in a coffeehouse
Chicago
Lubbock, TX
Midwest + Airplanes
nan
nan
nan
nan
Denver, CO
nan
Newark, Ohio
nan
Astoria, OR
nan
nan
nan
Eau Claire
Trapped in Baltimore.
Denver, CO
nan
Arlington,Tx
Chicago
nan
Around...
nan
nan
Raleigh, NC
nan
nan
The next President of Libya
The next President of Libya
nan
Duluth, MN
nan
Boston, Atlanta, or in-flight
woodstock, Ontario, Canada
woodstock, Ontario, Canada
Canada
Chicago
nan
nan
nan
nan
nan
nan
nan
Los Angeles, CA
Perdido Key, FL
Hampton Roads, Virginia
nan
San Diego, CA
nan
EVERYWHERE
Duluth, MN
Atlanta, GA
nan
nan
Raleigh, NC
Chicago
Duluth, MN
nan
nan
Valrico, Florida
Fort Lauderdale
Fort Lauderdale
Fort Lauderdale
Fort Lauderdale
Kansas City, KS
Lee's Summit, MO, @CityOfLS
Austin, Texas
San Juan, Brooklyn, LA
San Juan, Brooklyn, LA
Miami, New York, Boston
nan
Orlando, Florida
Raleigh, NC
InMyFeelings, IL
nan
Austin, Texas
nan
nan
Austin, Texas
nan
Everywhere You Wish You Were!
Trapped in Baltimore.
SF Bay Area
Dallas
nan
nan
nan
Austin, TX
nan
Albuquerque, NM
nan
nan
Birmingham, AL
nan
Kansas City MO
Fullerton, CA
Manchester, NH
Galt Gulch
Chicago
Chicago
Detroit
Around...
Manchester, NH
Galt Gulch
Boston, MA
nan
Kansas City
Kansas City, KS
Minneapolis, MN
Philadelphia, PA
Minneapolis, MN
Everywhere But Never Scared
Albany, NY
Minneapolis, MN
Minneapolis, MN
Florida
Chicago
Minneapolis, MN
Dallas, TX
DFW
cambridge, maryland
nan
Kansas City MO
nan
nan
nan
nan
South Florida
South Florida
Kansas City area
Minneapolis, MN
nan
Hopatcong, NJ
Hopatcong, NJ
The World is my Country
nan
South Florida
Detroit, Michigan
nan
South Florida
South Florida
nan
la
California
nan
Los Angeles, CA
Moultonborough, NH
nan
Kansas City MO
Logan Square, Chi / Austin, TX
Tennessee
Canada/US
ny
Kansas City MO
nan
Mustang, Oklahoma
DFW
California
Kansas City MO
Los Angeles
New Orleans, LA, USA
nan
Panama City, Florida
nan
Houston, TX
Whereever Yearbooks Are Born
Charleston, SC
ATL
New Orleans, LA, USA
Boston
Waterford, MI
Houston, TX
Washington D.C. / Northern VA
CT
CT
nan
NE Ohio
nan
nan
nan
nan
Vancouver, WA
nan
Somewhere mind in my business
Brooklyn NY
nan
Washington DC
Canada
Chicago
nan
Washington DC
Canada
Dallas Fort Worth
nan
Around...
Boston
darien, il
Carlsbad | CA
California/Maryland
Around...
darien, il
nan
nan
+ Las Vegas +
Near Los Angeles CA
Phoenix AZ
nan
KCMO
Washington, DC
nan
nan
nan
nan
nan
Southern California
Dallas, TX
nan
nan
nan
Nebraska
nan
Madison, OH
Gold Coast, Australia
KCMO
Indianapolis, Indiana; USA
nan
KCMO
Indianapolis, Indiana; USA
Indianapolis, Indiana; USA
Franklin, TN
Kensington, MD
Las Vegas, NV
Boston, MA
nan
California
Las Vegas
nan
nan
nan
Las Vegas
San Francisco, CA
nan
Philadelphia, PA
Littleton, NH
Redlands, CA
Las Vegas, NV
nan
nan
nan
Dodger Stadium | Disneyland
nan
Ozarks
suburbs of Pittsburgh....
nan
nan
nan
Reno, NV
La Jolla, CA
nan
nan
nan
Redlands, CA
nan
Philadelphia PA
nan
Chicago
nan
San Francisco, CA
nan
nan
London UK & USA
Boston, MA
nan
nan
Twin Cities
Las Vegas
nan
nan
nan
Chicago
nan
nan
DEN
San Francisco, CA
Boston, MA
nan
Chicago
Birmingham, AL
nan
WashingtonDC
nan
•New York•
WashingtonDC
WashingtonDC
New England
nan
nan
Missouri
New England
nan
Arlington, TX
Birmingham, AL
nan
nan
nan
Bellingham, Washington
wherever you need me to be.
Washington DC
Bloomington/San Francisco
nan
some where in massachusetts
Canada
nan
Baltimore,MD
RI
Denver, CO
Royal Palm Beach, FL, USA
nan
nan
Royal Palm Beach, FL, USA
CT
nan
The AZ Desert
nan
nan
nan
nan
Wilmington, North Carolina
D(M)V
nan
Redlands, CA
nan
RI when home.
nan
nan
nan
Rocky Mountains, Colorado USA
nan
Los Angeles
nan
Wilmington, North Carolina
Denver, CO
Raleigh, North Carolina
Columbus, OH
Columbus, OH
nan
Austin, TX
nan
Westminster, CO
Lone Star State
nan
insta : michellehamm0nd
nan
nan
Denver, CO
Overland Park, KS
Sac City, IA
nan
EIN: 27-0575748 -New York -USA
Phoenix AZ
Abu Dhabi
Abu Dhabi
Abu Dhabi
Abu Dhabi
Abu Dhabi
Redlands, CA
nan
nan
Des Moines, IA
nan
Chicago, IL
Columbus, OH
Boston, MA
nan
nan
Omaha, NE
nan
Grand Island, Nebraska, USA
nan
Boston, MA
nan
Grand Island, Nebraska, USA
Vineland & South Florida
nan
nan
Las Vegas
Saint Louis, MO USA
nan
Saint Louis, MO USA
Tipp City, Ohio
Atlanta, GA
Old Hickory, TN
Sunny Florida
RI
Everywhere
nan
nan
Atlanta, GA
Tallahassee, FL
Frederick, MD
Atlanta, GA
nan
Memphis
nan
Golftown, USA
nan
Oklahoma City
Englewood, Florida
Effingham, IL
nan
Tucson, AZ
nan
Oregon
Georgia
nan
nan
College Park, MD
Washington, DC
nan
Atlanta, GA
Phoenix, Arizona USA
Washington D.C.
nan
nan
nan
Nashville & Memphis
nan
Lincoln, NE
nan
Seattle, WA
Aurora, OH
boulder, co
Aurora, OH
Minneapolis
Aurora, OH
Aurora, OH
Kansas City
San Francisco, CA
Denver, CO
Denver, CO
Aurora, OH
STL
NY
Memphis
Boulder
Forest City, IA
Chicago by way of California
Golftown, USA
Happy Valley, Oregon
Kansas City, Missouri
nan
denver
New York, New York
nan
Brentwood, Tennesse, USA
Boulder
Baltimore
Chicago by way of California
Colorado
west allis
Chicago
Pennsylvania
nan
Tallahassee, FL
nan
Between FL & MN
nan
Cambridge, MA
nan
Cambridge, MA
North New Jersey
New York, NY, DC & Maryland
New England
Las Vegas, NV
Kansas City
nan
South Texas
nan
nan
Kansas City
Chicago, IL
Englewood, Florida
nan
Las Vegas, NV
nan
Bristol CT
Omaha, Nebraska
nan
nan
Colyell, Louisiana
nan
UTSA '13
nan
South
Arkansas, USA
Orlando, Florida
Brooklyn NY
nan
Ferndale, MI
MINNESOTA
nan
nan
Virginia.
nan
Baltimore, MD
nan
nan
seattle
Belton, Tx
nan
nan
nan
Austin
nan
SF Bay Area
Virginia.
Woodsboro, TX
USA
nan
nan
nan
Pennsylvania
North Carolina
nan
nan
Austin, TX
Denver, Colorado
USA
nan
Denver, CO
nan
México
nan
nan
The happiest place on Earth!
Fayette County/Oakland #901
nan
Salt Lake City
nan
nan
nan
nan
college
nan
nan
Sunny Florida
nan
Salt Lake City
Fremont, CA
nan
nan
Back in the Mitten
Dallas, Texas
nan
Houston, TX
nan
nan
ATX/LA
nan
nan
nan
Back in the Mitten
nan
Missouri
#BrownsNation
Washington, DC
nan
Southern Virginia!!!!!!
Baltimore
nan
Boston, MA
nan
nan
.kansas city.
Washington, DC
no where
no where
nan
nan
nan
Denver
nan
nan
Loomis, CA (Near SacTown)
Hanover, Maryland
Newark, NJ
Washington DC
nan
everywhere, all the time.
nan
nan
nan
nan
CA & MI
USA
Albany, New York
Brooklyn
Washington, DC
CA & MI
Texas
Brooklyn
Indianapolis
Bethesda MD
Dallas, Tx
Nashville, TN
nan
nan
#DFWLittleRockMempLAVegas#MS#
Two Guns, Arizona
nan
nan
Texas
boulder, co
nan
nan
San Diego, CA USA
Indianapolis
UTSA '13
Wichita
nan
Rockville, Maryland
Herndon, VA
Den10
nan
nan
nan
nan
31.708674,34.993824
31.708674,34.993824
nan
nan
lowkey
nan
nan
nan
Phoenix, Az
nan
Albuquerque, New Mexico, USA
Columbus, OH
nan
Ohio
Columbus, OH
Newark, NJ
nan
west allis
nan
nan
Cambridge, MA
Seattle, WA
Cranberry 16066/Bonita Springs
nan
nan
nan
Upstate New York
columbus
nan
Nashville, TN
nan
nan
nan
nan
Washington, DC
nan
nan
nan
nan
nan
Off the Road
Houston, Texas
Houston, Texas
Bethesda MD
Baltimore
nan
NYC | DC | Charlotte | Vegas
nan
nan
Philadelphia PA
Havertown, Pa.
Overland Park, KS
whore island
Nashville, TN
nola/beantown
nan
Gloucester, MA
Loomis, CA (Near SacTown)
Nashville, TN
nan
Loomis, CA (Near SacTown)
nan
Atlanta, GA
#PureMichigan
nan
minneapolis,mn
San Diego CA
Tallahassee, FL
Tallahassee, FL
nan
SoCal
San Diego, CA
Hershey, PA
nan
nan
Dallas, Texas
USA
Toronto (formerly NYC)
IN
Tallahassee, FL
Lockport, NY
nan
Boston, MA
nan
nan
nan
McLean, VA
nan
nan
nan
nan
nan
Nashville, tn
San Diego
Nashville, TN
nan
Chicago, IL
USA
nan
Fairfax, VA
Fairfax, VA
nan
Norfolk, VA
from the middle of america
Havertown, Pa.
nan
nan
N 30°23' 0'' / W 97°44' 0''
nan
New York, NY
nan
Chicago, IL
Tallahassee, FL
Orlando.South Beach.Vegas
Havertown, Pa.
Houston, Texas
Beysus
Tallahassee, FL
RI when home.
nan
Tallahassee, FL
Havertown, Pa.
Wilmington, North Carolina
nan
Chicago
Washington, DC
San Juan Island, WA
Tallahassee, FL
Tallahassee, FL
Tallahassee, FL
Newark, NJ
Tallahassee, FL
10/5/13
Tallahassee, FL
catch me if you can
Middletown, CT
Tallahassee, FL
Austin, TX
catch me if you can
Columbus OH
Los Angeles/SF/Palo Alto
nan
nan
Tallahassee, FL
Tallahassee, FL
Ohio & Kentucky
nan
Tallahassee, FL
Tallahassee, FL
nan
nan
Columbus OH
Hudson, NH
Columbus OH
nan
San Diego
Charleston, SC
Nebraska
AREA 51 NEVADA
Havertown, Pa.
nan
Houston, Texas
Buffalo, NY
Joe Louis Arena/Goggin Ice Ctr
San Diego
Aspiring Disney princess✨
St Louis
Buffalo, NY
Washington, DC/Austin, Texas
Lehigh Valley, PA
CT
Colorado
nan
nan
South Australia
Maine
Boston, MA
Memphis, TN
Austin, Texas
nan
Boston, MA
nan
nan
nan
nan
St. Louis
Atlanta
nan
Dallas, TX
nan
nan
chicago
North Bethesda, MD
Los Angeles, CA
Nwps CA.
Ohio
Helena-West Helena, AR
Upstate New York
nan
nan
nan
nan
New York
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
nan
Washington, D.C.
Tallahassee, FL
nan
Liberty Lake, WA
Murfreesboro, TN
Tallahassee, FL
Liberty Lake, WA
Rochester, NY
Indianapolis
City of Angels
Tallahassee, FL
Kansas City
Lockport, NY
Havertown, Pa.
Chicago, IL
Lockport, NY
Provo, Utah
Havertown, Pa.
nan
Kansas City
California
Fort Lee, NJ
South Carolina/Nashville
Central Ohio
Long Island, NY
nan
명동서식 37.56638,126.984994
nan
St. Louis, MO
nan
Fort Lee, NJ
nan
Fort Lee, NJ
Texas
nan
Nebraska
Denver, CO
Chicago, IL
Denver, CO, USA
NYC/Columbus/Chicago
nan
Ronkonkoma, NY
Salt Lake City, Utah
Douglas, MA
Denver
nan
Virginia
Branson, MO
Virginia
Chicagoland
nan
nan
nan
Denver
nan
in ur base killin ur tw33tz
St. Louis
nan
nan
nan
nan
Dallas, Texas
nan
Beverly Hills, CA 90212
Indianapolis, IN
nan
nan
washington, d.c.
Volunteer State
Roxbury Crossing, MA
Sacramento
Spring, TX, Chicago and others
Bay Area
Kansas City, KS
Littleton, CO
St. Louis, Missouri USA
San Diego
North Texas
Denver
nan
10/5/13
nan
Roxbury Crossing, MA
Denver
Washington, DC
Littleton, CO
Philadelphia, PA
nan
Florida
Littleton, CO
nan
nan
nan
San Diego, CA
Roxbury Crossing, MA
Indiana...Our Indiana
Austin
Florida
nan
Florida
nan
nan
Florida
Florida
nan
Florida
Chicago
San Diego, CA
North Texas
nan
JerseyNY✈ATL
Lost In The Ether
New York, NY
NH
nan
Colorado
Louisville, KY
San Diego, Ca
Atlanta, GA
nan
Salt Lake City, UT
nan
KC
Murfreesboro, TN
New Jersey / Munich
Nashville, TN
nan
nan
KC
Abuja
The Universe
nan
Baton Rouge, LA
nan
nan
Oklahoma City, OK
Denver Co
Egg Harbor, WI
Nashville, TN
Washington DC, New York
nan
musician and music enthusiast
Columbus, Ohio
nan
Farmington, Utah
Dallas, TX
Ontario, Canada
Washington DC, New York
nan
Salt Lake City
Singapore
nan
nan
Rhode Island ,Mass,CT
nan
LA // DC // LA
Austin, TX
nan
Long Island, NY
Dallas, TX
Long Island, NY
Dallas, TX Memphis, TN, MS
LA // DC // LA
Oklahoma City
Salt Lake City
Los Angeles, California
nan
nan
nan
Albany, NY
Nebraska
nan
Boston, MA
Boston, MA
nan
nan
CT
The Universe
Boston, MA
North Bethesda, MD
The Universe
nan
nan
nan
nan
nan
#PureMichigan
Barrington, Rhode Island
nan
nan
43.012983,-78.724003
Columbus, Ohio
Washington, DC
Ohio
nan
Ohio
St Louis
South Carloina
Den10
nan
Omaha, NE
Englewood, Florida
nan
nan
Omaha, NE
Louisville, KY
nan
Narragansett, Rhode Island
nan
Branson, MO
Somerville, MA
nan
nan
Louisville, KY
New York City
nan
nan
nan
Liberty Lake, WA
Oregon
Indianapolis, IN
St. Louis, MO
St Louis, MO
Orleans/Tarpon Springs/London
Henderson,NV
nan
New York
Chicago, IL
Columbus, OH
Columbus, OH
north bend, wa
Texas
Iowa State University
Albuquerque, NM
Rhode Island ,Mass,CT
Atlanta, GA
The Ohio State University
Denver, CO
Charleston, SC
Charleston, SC
City of Angels
Charleston, SC
City of Angels
HOUSTON
nan
nan
nan
Kentucky, USA
Los Angeles
San Francisco, CA
Los Angeles
nan
San Francisco, CA
Chicago
USA
India ✈️ Chicago ✈️ D.C. ✈️ FL
Philadelphia, PA
new york
Dallas
Washington, DC
Dallas, TX.
nan
Raleigh
nan
FORT WORTH TEXAS!
Philadelphia, PA
nan
iPhone: 35.300907,-85.900749
Philadelphia, PA
USA, USA!
Chicago
nan
Douglaston, NY
Tucker, GA
nan
Silicon Valley, NoLa, Boston
Douglaston, NY
nan
nan
|| san antonio, texas||
Winter Park, FL
Chicagoland
Austin, Texas
nan
Atlanta
nan
Omaha, NE
Omaha, NE
Omaha, NE
Fort Worth, TX
nan
nan
Ohio
Fort Worth, TX
Chicago
nan
¢нιℓℓιи αт αи ιD ¢σи¢єят
nan
nan
nan
Winchester | Northend Ma
nan
nan
nan
Sacramento, CA
Washington, D.C.
Austin, TX
Where You Wanna Be
Nashville
nan
Nashville, TN
Somewhere in Tennessee
nan
nan
nan
Somewhere in Tennessee
Atlanta
Los Angeles
nan
nan
nan
nan
Dallas, Texas
Memphis, Tennessee
ILLINOIS
nan
Springfield, MA
San Antonio, TX
nan
nan
nan
nan
Highlands Ranch, CO
nan
nan
310
San Jose, CA
Kansas City, MO, USA
nan
Kentucky
310
nan
nan
nan
camas, wa
nan
camas, wa
USA, USA!
TX | CA
Texas
nan
BRKLYN, NY
nan
nan
Nashville, TN
Las Vegas, NV
Philadelphia, PA
Dodger Stadium | Disneyland
nan
nan
nan
nan
chicago, il
nan
nan
TX | CA
Washington DC via L.A.
Dodger Stadium | Disneyland
looking for alaska
Hogwarts
Scottsdale, AZ
St. Paul, MN
Scottsdale, AZ
Dodger Stadium | Disneyland
nan
city of Lost Angels
TX | CA
Scottsdale, AZ
Opelika, Alabama
looking for alaska
Boston
Dodger Stadium | Disneyland
USA, USA!
Hogwarts
New York City
San Francisco
Atlanta
Atlanta
Dodger Stadium | Disneyland
Dirty South Jerz
Chicago, IL
nan
San Francisco
nan
New York City
TX ΚΔ
San Diego
nan
Bellevue, WA
Ottawa Canada
Austin, TX
Chicago
Chicago
Dallas, TX
yuma az
Kansas City, MO
In my mind
Rochester Hills, MI
Washington, DC
Sittin on the dock of the bay
nan
Atlanta, GA
Chicago
Milwaukee, WI
WORLDWIDE
WORLDWIDE
Northern UT
Northern UT
Astoria, New York
san diego, CA
Rochester Hills, MI
nan
SF by way of NY and UNC.
nan
Beaverton, OR
Liverpool, NY
washington, dc
nan
University City, MO
SF by way of NY and UNC.
Austin, TX
nan
nan
Annapolis, MD USA
Glen Burnie
Zephyrhills, Florida
nan
nan
Astoria, New York
In my mind
nan
( Maryland | North Carolina )
johnson city tn
denver, co
Nashville, TN
nan
nan
New Mexico
nan
Las Vegas, NV
camas, wa
camas, wa
nan
Pocono Raceway
St. Louis, MO
South ~O-H-I-O~ Side
Austin, TX
Kansas City
San Francisco, CA
Boston
Seattle (duh!)
nan
Milwaukee wi
Everywhere
Phoenix, Arizona
nan
nan
nan
Tempe, AZ & World Wide
nan
Boston
nan
nan
Everywhere
San Francisco
Hoboken, NJ
nan
nan
nan
nan
nan
nan
www.twitch.tv/sovindictive
nan
nan
Raleigh, NC
Douglaston, NY
Hoboken, NJ
Ohio (by way of Nebraska)
nan
nan
nan
nan
Dallas, TX
Indiana
Ohio (by way of Nebraska)
Grand Rapids,MI & Orlando, FL
nan
Michigan
10 Miles South of Nowhere
nan
Columbus, OH
nan
nan
nan
nan
nan
nan
Grand Rapids, MI
10 Miles South of Nowhere
nan
nan
PHX, AZ
PHX, AZ
nan
PHX, AZ
nan
Milwaukee, WI
Suwanee, GA
Colorado
Indianapolis, IN
Baltimore, MD
Tempe, AZ & World Wide
Baltimore, MD
Baltimore, MD
In my mind
nan
Tempe, AZ & World Wide
nan
nan
Missouri, unfortunately.
nan
Suwanee, GA
Reno, NV, USA, Earth, Sol
nan
Missouri, unfortunately.
D{M}V
Portsmouth, NH
Missouri, unfortunately.
St. Louis
Denver
nan
Portsmouth, NH
nan
San Francisco
Austin, TX
Orlando Florida. (Winter Park)
nan
nan
Merica
nan
nan
Las Vegas
nan
New York, NY
nan
nan
nan
nan
nan
nan
nan
boston, ma
New York City
North Carolina
nan
San Diego, California
Nashville, TN
a cell tower
nan
N 30°23' 0'' / W 97°44' 0''
Chicago
nan
Seattle
nan
City of Angels
nan
Hollywood, CA
Ottawa,Canada
Springboro, Ohio
Lake Arrowhead, CA
Queenstown, MD
nan
camas, wa
~~ Rhode Island ~~
Austin, TX
Kansas City, Missouri
nan
Philadelphia, PA
'Zona
'Zona
Philadelphia, PA
'Zona
nan
nan
nan
ÜT: 34.621818,-82.614032
nan
nan
nan
Minnesota, USA
Raleigh, NC
Iowa
nan
nan
nan
Northern Virginia
nan
Rochester, NY
Rochester, NY
Chicago
Chicago
Atlanta
Chicago
ÜT: 33.648576,-117.898653
San Antonio, TX
Rochester, NY
nan
nan
nan
nan
New York City/San Antonio
nan
Nashville/Franklin
nan
nan
nan
Columbus, Ohio
nan
johnson city tn
nan
nan
Plain Ol Plano, TX
Lincoln, RI
nan
nan
Chapel Hill
Uvalde, TX
Plain Ol Plano, TX
Texas
sacramento.ca.usa
Columbia, Mo.
Chapel Hill
catch me if you can
Chicago, IL
bev. hills!
nan
nan
McKinney, Texas
indiana
On It
Austin, TX
On It
US
Los Angeles
San Diego
We know all so follow -A
We know all so follow -A
Calgary AB Canada
nan
Chicago
nan
nan
nan
nan
nan
Chicago
Washington DC
Tampa Bay (Chicago native)
Washington DC
Tampa Bay (Chicago native)
New York City
Gridley, CA.
Florida
schuhm@wjz.com
san diego, CA
nan
Silicon Valley, CA
New Orleans, Louisiana
Houston
Omaha
Boca Raton, FL
iPhone: 38.639580,-90.237061
Atlanta, GA
Williamsport, Pa
SEA
NYC or a plane
Somewhere living life....
nan
Los Angeles
nan
DC
nan
McKinney, Texas
Round Rock, TX
Texas
Maine
nan
Los Angeles, CA
Pennsylvania
nan
nan
Chicago--Savannah--Atlanta, GA
West Coast
West Coast
Denver, CO. USA
West Coast
Bay Area, CA
Austin, Texas
nan
Round Rock, TX
nan
nan
Denver, Colorado
nan
nan
nan
nan
West Coast
San Jose, CA
Los Angeles, CA
Las Vegas
Los Angeles, CA
Houston, TX
nan
nan
508--954--305--615
nan
Columbus, Ohio
nan
Colorado
CT
nan
CT
CT
CT
CT
nan
Duluth, GA
nan
nan
Barrie | Ontario | Canada
nan
13Curious
nan
San Francisco
NorCal - Eugene - Indianapolis
United States
minneapolis, mn
Space (till back in the PH)
NM TX PA ¯\_(ツ)_/¯
minneapolis, mn
nan
Los Angeles
nan
nan
nan
nan
nan
nan
nan
nan
nan
Los Angeles, CA
nan
nan
Tempe, AZ
Chicago
Gone West
nan
nan
Gone West
nan
Dallas, TX
UNICEF NYHQ
nan
CT
Worldwide
nan
nan
Sutherlin, Oregon
Virginia
Pittsburgh, PA
Seattle
florida
La Jolla, California
San Antonio, Texas
nan
florida
florida
Chicago
Chicago
Buffalo, NY
Vegas
Los Angeles
iPhone: 44.912468,-93.318619
nan
Canada
Long Island, NY
Boston
Fort Worth, Texas
Minnesota
nan
Clearwater / Bethlehem
Live Free or Die
Ithaca, NY
Duluth, GA
Northwest
pittsburgh pa
iPhone: 44.912468,-93.318619
Austin, Texas
Dallas, TX
Los Angeles
Window Rock, Arizona
Minneapolis, MN
Columbus, Ohio
California
nan
NY
nan
ʞᴚoʎ ʍǝu
nan
ʞᴚoʎ ʍǝu
nan
nan
nan
Buffalo, NY
ʞᴚoʎ ʍǝu
ʞᴚoʎ ʍǝu
ATL
Canton, MA
Roseville, CA
Melbourne, Australia
Alabama
nan
Michigan
CT
Orlando, Florida
Alabama
nan
nan
dallas,texas
Scottsdale Arizona
Long Island
Long Island
San Antonio, Texas
Buffalo, NY
nan
nan
nan
nan
nan
nan
nan
Rochester, Michigan
Houston, TX
Canada
Powell, TN
nan
Denver, CO. USA
Indianapolis, USA
601,303,404,✈️ 400Blk
Las Vegas, NV
nan
Denver, CO
Denver, CO
nan
nan
Little Rock, AR
Houston, Texas
nan
HTX
nan
Los Angeles, CA
Los Angeles, CA
Omaha, NE
nan
nan
United States
Phoenix, AZ
Rochester Hills, MI
Dallas, TX
nan
nan
Chicago
ATLANTA
nan
nan
Milwaukee
Wisconsin
Chandler, AZ
Rockwall, TX
Oklahoma
The Bull City, NC
nan
Boston, MA
nan
nan
nan
nan
nan
Lake Buena Vista, Florida
Lake Buena Vista, Florida
nan
California
The Hill!!!
they/them
Rite behind my $$
1/3 whenever november
they/them
nan
they/them
TX // IN
nan
Maryland
nan
nan
nan
nan
nan
Fayetteville North Carolina
nan
Bay Area, California
californyeah
Bay Area, California
nan
Chicago, IL
Washington | Tampa | Baltimore
Maryland
washington, dc
Denver
Fayetteville North Carolina
indiana
Buffalo, NY
Washington, DC
nan
San Diego, CA
nan
nan
nan
Nashville, TN
nan
Nashville, TN
nan
nan
Washington, DC
nan
nan
Scottsdale, AZ
nan
nan
Los Angeles/Chicago
Orange County, CA
Warren, OH
Canton, MA
Canton, MA
Canton, MA
Warren, OH
Fort Worth, TX
Fort Worth, TX
nan
nan
Colorado
nan
Zamunda
Scottsdale, AZ
nan
Centerville, TN
nan
Southern California, USA
Bay Area - Cali
nan
D.C. Metro Area
NH, United States
tampa, fl
Rockwall, TX USA
Maryland
nan
NH, United States
nan
Las Vegas
Las Vegas
nan
North Minneapolis
Hockeytown, USA
MA
nan
nan
nan
nan
nan
nan
nan
Chicago, IL
CT
Dallas, TX
nan
nan
Global
nan
nan
ÜT: 40.819919,-73.864869
nan
Vegas
nan
nan
nan
Miami
nan
nan
nan
ATL
Los Angeles, CA
nan
Los Angeles
Dallas tx
here there many wheres
nan
Maryland and Washington D.C.
Houston, Texas
Rogue, USA
nan
http://goo.gl/eZTfG
nan
Maryland and Washington D.C.
Ottawa,Canada
Oklahoma
nan
Austin, TX
Where the wild things are
Brawley
Austin, TX
Ottawa,Canada
Nashville by way of New York
Houston, Texas
murfreesboro
nan
nan
Beaufort, South Carolina
Austin, TX
Cleveland, Ohio
nan
Houston, Texas
Miami
Kirkwood Missouri
Austin, TX
Everywhere But Never Scared
The Wild Blue Yonder
los angeles, ca
Cleveland, Ohio
California
nan
nan
nan
Tampa / St Petersburg ,Florida
Sacramento, CA
Atlanta, Georgia
nan
Toronto, Canada
Tennessee
CA
Tennessee
Boston (sort of)
PostVegasDepression in Texsus
Texas
Tennessee
nan
nan
Tennessee
Texas
ʞᴚoʎ ʍǝu
nan
ʞᴚoʎ ʍǝu
The Sunshine State
2/26/14
Richmond and Virginia Beach
TRI CITIES (TRI)
Ig:@/imaginedragoner
ABQ
Vegas
CT
Florida, Arizona, Worldwide
Sacramento, CA
nan
Baltimore, MD/Wash. DC area
Miami, FL
nan
Everywhere But Never Scared
PlanoTexas
Salt Lake City, UT
nan
Windsor& Los Angeles-Cali Life
Nashville, tn
Austin, TX, USA
Nashville/Franklin
San Francisco
San Francisco
Austin, TX, USA
Chicago, IL
Austin, TX, USA
New York, NY
Nashville/Franklin
Worcester, MA
Worcester, MA
Somewhere over the rainbow.
Somewhere over the rainbow.
Somewhere over the rainbow.
Most States in US
Somewhere over the rainbow.
Nashville Tennessee
Somewhere over the rainbow.
Somewhere over the rainbow.
Somewhere over the rainbow.
nan
nan
Somewhere over the rainbow.
Somewhere over the rainbow.
Somewhere over the rainbow.
Somewhere over the rainbow.
DFW
Somewhere over the rainbow.
Somewhere over the rainbow.
Zephyrhills, Florida
Somewhere over the rainbow.
Somewhere over the rainbow.
Nashville, TN
Memphis, TN
Seattle, WA
Suffolk County, New York
Central Jersey
Atlanta, GA USA
nan
College Station, TX
Austin, TX
✈ Check ☟ out ☟ my blog ☟
Suffolk County, New York
nan
Dallas,TX
Nashville, TN
FL, US of A
nan
nan
Washington, D.C.
Tennessee Original
nan
nan
Dodger Stadium | Disneyland
On the 757 Right Coast
Nashville, TN
Nashville, Tn
nan
nan
nan
nan
nan
Marina Del Rey
Nashville by way of New York
nan
Oakland, CA
nan
Oakland, CA
nan
Clover, SC
nan
Duke University Class of 2018
Spokane, WA
Roseville, California
Tampa / St Petersburg ,Florida
Washington D.C.
nan
nan
42.102725,-87.970154
nan
nan
nan
Oakdale, CA
nan
Nashville
Raleigh, NC
Denver, CO
nan
nan
nan
Hanging with Carmen Sandiego.
Hanging with Carmen Sandiego.
Dallas
nan
nan
Connecticut
nan
Indiana
CO to CA
nan
CT
CT
nan
nan
Austin, TX
washington
Rogue, USA
nan
Park City
nan
nan
Boston
nan
New Orleans/Dallas
nan
Hanging with Carmen Sandiego.
Most likely Costco
nan
Kalispell Montana
nan
Los Angeles, CA
Tennessee
nan
nan
Tampa
FL, US of A
nan
nan
nan
Downtown Dallas, Texas
nan
CHICAGO-STL
Classified ✈️✈️✈️
Atlanta
New Hampshire
nan
Oklahoma City
La Florida
Memphis, TN
Memphis, TN
Memphis, TN
soundcloud.com/ibhoody
nan
nan
nan
0/5 0/4
Lubbock Texas
HOOSiER STATE
SMALL TOWN, USA
Toronto
nan
nan
Portland,OR--Tempe,AZ
Englewood, Florida
nan
nan
Memphis, TN
nan
Midlothian, Virginia
nan
New York, NY
I forgot - Earth Maybe??
Franklin, TN
New York, NY
Eastern Time, United States
New York, NY
boston, ma
New York, NY
Ole Miss • ATL
New York, NY
Englewood, Florida
Fort Lauderdale
Dallas
Nashville,TN
nan
nan
Nashville, TN
Seattle area
nan
Do as you wish, with integrity
Nashville by way of New York
Nashville by way of New York
Nashville by way of New York
Nashville by way of New York
Rite behind my $$
Nashville by way of New York
Houston TX
nan
Sunrise Beach, MO
Chicago
College Station, Texas
Sunrise Beach, MO
ÜT: 35.935585,-86.886267
Tucson, AZ
San Antonio
Los Angeles, New Orleans!
nashville, tn
nan
on the water☀
Oakdale, CA
Campbell, CA
St. Petersburg
nan
Boston
Baltimore, MD
CSB/SJU
Boulder
Michigan
murfreesboro
Nashville, TN
NASHVILLE,TN
http://goo.gl/eZTfG
CSB/SJU
nan
Tucson, AZ
nan
nan
http://goo.gl/eZTfG
Sunrise Beach, MO
nan
D.C.
nan
San Antonio
nan
Nashville Tennessee
nan
Nashville Tennessee
Park City
Nashville Tennessee
Maryland
Palo Alto, California
nan
nan
Washington, DC
D.C.
nan
nan
Washington, DC
nan
baltimore, md
Nashville, TN
East Coast
dallas tx
Durham, NC
Des Moines, Iowa
CT
Sunrise Beach, MO
TX
nan
CO to CA
US
USA
San Francisco, CA
Nashville, TN
nan
nan
nan
Santa Ana, California
Nashville
New Orleans/Dallas
KCMO
Nashville, TN
TX
Chicago, Il
nan
Cleveland, Ohio
Los Angeles, CA
nan
San Francisco, CA
Cleveland, Ohio
Washington DC
Nashville, TN
nan
Nashville, TN
nan
nan
nan
Columbus, OH
nan
Michigan
Michigan
nan
nan
Michigan
Austin, TX
nyc/nashville/san diego
New Braunfels, TX
Cambridge, MA
nan
nan
nan
USA
San Francisco, California
212 by way of the 504.
NYC
Indy
Austin, TX
Here and there.
Flowood, MS
nan
Houston, Texas
CT
Memphis, TN
nan
Where you want to be!
Memphis, TN
San Francisco, CA
San Antonio, TX
San Francisco, CA
Nashville, TN
Southern Virginia!!!!!!
nan
nan
Atlanta Georgia
Washington, DC
Boston, MA
nan
Las Vegas, NV
San Francisco, CA
ÜT: 32.259172,-110.788252
Las Vegas, NV
Las Vegas, NV
nan
Washington DC
Washington DC
Washington DC
Cleveland, Ohio
Washington DC
Washington DC
Huntsville, AL
nan
Orchard Park, NY
iPhone: 36.205651,-86.695243
Tampa
Château d'If
Columbus, OH
Arlington, VA
Silicon Valley
Las Vegas, NV
nan
nan
nan
Las Vegas, NV
nan
nan
Nashville, TN
Nashville, TN
SMALL TOWN, USA
nan
Nashville, TN
Nashville, TN
Nashville, TN
Pittsburgh PA
nan
Minneapolis, Minnesota
nan
Austin, TX
Overland Park, KS
Delray Beach, FL
nan
nan
nan
Nashville,TN
Nashville,TN
nan
Cambridge, MA
Brooklyn, NY
Cambridge, MA
Cleveland, Ohio
Ellicott City, Maryland
Château d'If
Château d'If
Philly
Chicago, IL
Fort Lauderdale
Nashville,TN
Château d'If
nan
Michigan
nan
Midvale, UT
nan
Chapel Hill, NC
AZ - USA
Nashville, TN
Delray Beach, FL
St. Louis
nan
Dallas, TX
Nashville, TN
New York
Lawrsh Ainjellush, CA
nan
nan
nan
Toronto
Nashville, TN
New York, New York
USA
Silicon Valley
Texas
nan
Nashville, TN
TN
nan
nan
nan
Fort Lauderdale
Nashville, TN
Silicon Valley
District of Corruption
Nashville, TN
Nashville, TN
Midlothian, Virginia
Broomfield, CO
nan
Northridge, CA
New York
USA
nan
orlando
London, Ontario, Canada
Chicagoland Area
Nashville,TN
nan
Brooklyn, NY
London, Ontario, Canada
Las Vegas
San Francisco, CA
Phoenix, AZ
Rhode Island
Nashville,TN
Dallas, TX
nan
nan
nan
nan
Downtown Dallas, Texas
Michigan
America
USA
nan
nan
Los Angeles, CA
east brunswick, nj
Georgia
Georgia
Georgia
Georgia
nan
Logan International Airport
Boston, MA
nan
SF Bay Area
nan
Chicago
nan
nan
1/1 loner squad
nan
nan
nan
nan
Somewhere eating swine...
nan
nan
nan
Somewhere eating swine...
nan
nan
Canton MA
ÜT: 40.907502,-73.85468
nan
Boston, MA
nan
nan
In Oakland; Hoosier at heart
Boston, MA
nan
Port au Prince, Haiti
Stoughton, MA
New York-ish + Airport Bars
nan
Seattle
nan
NYC
Seattle
US
nan
Newcastle, Uk
nan
Inter/Outer-Continental U.S.
Bronx, NY / Destin, Fl
Boston, MA
Newcastle, Uk
brooklyn, ny, us
Miami, New York, Boston
anaheim
anaheim
D{M}V
nan
nan
MA
nan
nan
nan
where the Bulls/Bears play.
N 40°39' 0'' / W 73°55' 0''
l be where l'm at
Washington, DC
All over ...
Citizen of the world dahhhling
COMPTON/AZ
All over ...
All over ...
nan
Las Cruces, NM
nan
Boston, MA
nan
nan
Boston, MA
iPhone: 40.955353,-73.813942
NY,NY
Buffalo, New York
Vermont
iPhone: 40.955353,-73.813942
NY,NY
Boston
New York/New Jersey
nan
New York, NY
South Florida
New York, NY
New York, NY
New York, NY
central mass
central mass
nan
Buffalo
nan
nan
nan
nan
nan
Dubai
boston ma
nan
KFDK
Austin
Boston, MA
Logan International Airport
Englewood, Florida
Fort Lauderdale, FL
New York
NYC
Southern California
Portland, Maine
nan
ClayCo ATL
nan
nan
nan
nan
New York City
nan
NYC
NYC
Philadelphia/Cali
Boston, MA
805 CA
Boston, MA
805 CA
22nd Century
DC SF
Portland, Maine
Portland, Maine
Portland, Maine
Portland, Maine
nan
nan
nan
nan
nan
nan
nan
Portland, Maine
Portland, Maine
Portland, Maine
nan
Portland, Maine
nan
Logan International Airport
San Francisco, CA
Logan International Airport
Logan International Airport
New York
Los Angeles, CA
Logan International Airport
Logan International Airport
Logan International Airport
Somewhere being productive
nan
Long Beach, California
The Bronx || Inwood
Washington, DC 20003
Olympia, WA ✈ Charleston, SC
Los Angeles, CA
NYC/RVC/CHS/WASH DC
NYC/RVC/CHS/WASH DC
Los Angeles, CA
Everywhere
Phoenix AZ
nan
Northeast by SoCal by Carolina
Somerville, MA
Long Island, NY
nan
Long Island, NY
nan
Valsayn, Trinidad
nan
nan
Boston, Austin, Kansas City
Boston, Austin, Kansas City
Isle of Palms, SC
nyc
650/415/510
Stratford, CT
nan
ÜT: 40.645173,-73.898268
nan
サマセット、ニュージャージー州
Houston, TX
Fl...oating off into space
nan
Los Angeles, CA
hermosa beach california
Los Angeles, CA
Brooklyn, NY
Memphis (901-683-0989)
Will Work For Music
Memphis, TN
Los Angeles, CA
the tristate
nan
Connecticut
nan
UK
nan
Philadelphia, PA
NY
Los Angeles, CA
Los Angeles, Calif.
Long Island
Los Angeles, Calif.
New York
Portland, Maine
The Road to Damascus
Long Island
nan
nan
V I R G I N I A ! ! ! *804*
Somewhere
Brazil
Somewhere
nan
BWI
nan
Honolulu, Hawaii
UK
Brooklyn, NY
Slum Village, NY
Los Angeles
Brooklyn, NY
usually home
London UK & USA
Austin, TX
Brooklyn, NY
MichaelKay Ward DeLa Hoya HHH
Bey-Land. Eagle Nation.
Brooklyn, NY
Glasgow
Miami Beach FL
at Lil Kim's House
Boston, MA
New Orleans
UK
Boston, MA
instagram:Torrinichelle
Wherever the bacon is, USA
Cadiz/Louisville, Kentucky
nan
CA/NJ
Flexin in Zamunda
St. John's
Melbourne, Aus
Portland, OR
Boston, MA
nan
Victoria, BC, Canada
Boston, MA
WAITIN AT THE DOE
nan
nan
Phoenix, AZ // NJ
Bushmansted
nan
nan
nan
nan
D(MD)V ✈️ NYC ✈️ Germany
nan
Brooklyn, NY
nan
nan
nan
Tiger Town
Joja
Union City, CA
nan
Courtside or the 50yd Line
Memphis to Alabama.
Brooklyn, NY
nan
Sunny Cali by way of NYC
new york
nan
THE ROCK - hive
WNY
Floating in the Pacific
Ft. Lauderdale-Davie, Fl
Kansas City/Milwaukee
Bawston
dayton ohio
Poughkeepsie,NY-Manayunk,PA
Logan International Airport
Tallahassee, FL
El Paso, TX
New York, NY | Washington, DC
nan
FL Girl ✈️ DC Livin
Queens, New York
nan
nan
Baltimore, Maryland
Columbia, SC
New Yawk
nan
nan
Eternity|GOMAB
Denmark
College Park, MD
Logan International Airport
Friend Zone
Logan International Airport
Los Angeles, CA
Logan International Airport
Logan International Airport
Logan International Airport
Logan International Airport
Gates Ave- Brooklyn
Detroit, MI
nan
FL
No soy de aquí ni soy de allá
Philly
nan
NYC
Land of the ✌️free✌️
NJ and NYC
nan
Tally
nan
Vuhginyah
Boston, Austin, Kansas City
From Capitol Heights With Love
nan
nan
The Friendzone
nan
kingston, jamaica
nan
ON.
1 of the 50
oh so magical.
Washington D.C. / Chicago
ÜT: 33.464681,-84.173362
Raising Through 7
some where in massachusetts
DMV
nan
Thanks for asking
nan
Buck Guy City
Manhattan , New York
Worldwide
NY, LA
nan
some where in massachusetts
nan
Ga, DC, Kemet
~FlighT SchooL~
SF - NYC
nan
nan
the left coast
MPLS
In your liquor cabinet.
i'm only human
Jersey bred, Atlanta resident
in the wind...
interstellar
Raleigh, NC
nan
nan
Binghamton University
New Brunswick, NJ
DMV/CLE
nan
Where there is love..
nan
south gate, california
Dallas, Tx USA
vᴧ
nan
nan
Austin, Texas
nan
New York City
New York, NY
Boston, MA
New York City
Wall Street • Manhattan • NYC
California
ÜT: 28.217707,-81.45223
nan
Out There
nan
561 ✈️ da globe
San Francisco
... (shrugs)
RVA-804\757
Austin
nan
KCMO - University of Missouri
OAKLAND CALIFORNIA
Morning Wood Terrace
SoCalifornian in OH
the road less traveled.
New York City
USA
Pittsburgh — now & forever
New York, NY
Home: IAD, College: JFK
nan
nan
Chapel Hill, NC
Jupiter
Tempe, Arizona
greater los angeles
nan
Orange County, Ca.
KΔ. Boston.
NYC
Bangor, Maine, USA
Queens New York City
STX ✈️ MIA
New York City
New York City
nyc
Burlington, Vermont
New York City
UK
Bronx, NY
Burlington, Vermont
Boston, Austin, Kansas City
OP NY
Boston, Austin, Kansas City
NEW YORK
USA
Boston, MA.
NEW YORK
Boston, Austin, Kansas City
Burlington, MA
Bronx, NY
Boston, MA.
Boston, MA
USA
Auburn, ME
USA
Wien
Boston, MA.
USA
Boston, Austin, Kansas City
Chicago IL
Cape Cod Ma
Los Angeles, Calif.
nan
Salt Lake City, Utah
Muttontown, NY
Brooklyn, NY
Brooklyn, NY
nan
Puerto Rico
Muttontown, NY
nan
Puerto Rico
Abu Dhabi
Muttontown, NY
Boston, MA / Stockbridge, MA
Massachusetts
Cambridge, MA
Cambridge, MA
Boston, MA
A Galaxy Far Far Away
Cambridge, MA
nan
Plymouth, MA
Boston, MA
nan
Boston, MA
nan
nan
NYC/Austin
nan
nan
nan
gathering just enough moss
Gainesville, Fl
Washington DC
nan
nan
nan
nan
Gainesville, Fl
nan
nan
Boston
Long Island, NY
Lexington, MA
40.41,-73.19
Long Island, NY
nan
Puerto Rico
Las Vegas
California
SF
nan
nan
New York City
nan
NYC
New York
nan
nan
nan
nan
nan
nan
Boston
OP NY
OP NY
Right Coast
Right Coast
Connecticut
Planet Brooklyn
Planet Brooklyn
Provincetown Ma
Planet Brooklyn
Provincetown Ma
nan
Brooklyn, NY
Right Coast
nan
Right Coast
New York City
Logan International Airport
Right Coast
Hardly home but always reppin #562
Brooklyn NY.
Los Angeles, California
nan
NYC | NOLA
nan
New York, NY
NYC | NOLA
Austin + Brooklyn
Brooklyn NY.
Long Island - Arizona
nan
nan
San Francisco, California
Texas
nan
San Francisco, California
NYC | NOLA
NYC | NOLA
NYC | NOLA
NYC
indoors
nan
New York
Mexico-Colombia
LA/NY
nan
indoors
ÜT: 18.463449,-70.003608
Boston, MA
Boston, MA
New York
Canadian planted in USA
nan
nan
nan
New York City
nan
Boston, MA
New York
Brooklyn
Texas
Boston, MA
New York
nan
ÜT: 18.463449,-70.003608
New York
nan
Los Angeles, CA
1-800-JETBLUE
nan
nan
nan
Los Angeles, CA
ÜT: 18.463449,-70.003608
New York City
nan
nan
New York City
Gotham
Long Island - Arizona
Gotham
San Francisco, CA
Gotham
nan
Gotham
Boston, MA
San Francisco, CA
Brooklyn, NY
Brooklyn, NY
NYC ❄️ / COL
nan
Boston
nan
Brooklyn, NY
Boston
New York
REVERE MA
nan
Boston
Brooklyn =)
Boston
New York
Brooklyn =)
Brooklyn =)
nan
Brooklyn =)
Long Island - Arizona
Merrimack NH
Stratford, CT
Chicago
massachusetts yall~~~~~~~簡単に行く
Washington DC Metro
nan
nan
3Ø4
nan
nan
Long Island - Arizona
nan
nan
nan
Long Island - Arizona
3Ø4
nan
Greater Boston Area
nan
nan
Sarasota, FL
massachusetts yall~~~~~~~簡単に行く
Chicago
nan
nan
nan
3Ø4
3Ø4
nan
New York
nan
nan
nan
nan
Sarasota, FL
nan
42.3806° N, 71.2350° W
3Ø4
nan
nan
Chicago
nan
Chicago
Right Coast
nan
New York
nan
nan
breukelen, ny
nan
nan
nan
Right Coast
nan
Washington, D.C.
New York / Bahamas
Boston, MA
New York, NY
Provincetown Ma
nan
Providence, RI
Providence, RI
Providence, RI
Washington, D.C.
New York City
Washington, DC
Washington, DC
Brooklyn, NY
Washington, DC
nan
nan
New York City
Long Island - Arizona
nan
Miami, FL
Long Island - Arizona
nan
Ft. Lauderdale, FL
Port-au-Prince, NYC.
Long Island - Arizona
nan
New York City
Miami, FL
nan
Palo Alto CA
nan
nan
New York, NY
#Titletown
N Y
the burg nj
the burg nj
the burg nj
N Y
the burg nj
the burg nj
NYC
NYC
NYC
NYC
NYC
NYC
NYC
NYC
NYC
Washington, D.C.
San Francisco
Sarasota, FL
New York
Sarasota, FL
San Francisco
Washington, D.C.
Washington, D.C.
Fort Lauderdale
Boston
nan
New York, NY
New York/Long Beach
nan
nyc
Sarasota, FL
nan
nyc
nyc
nyc
nan
New York, New York
nan
Long Island - Arizona
nan
nan
LA CA USA
Long Island - Arizona
New York, New York
nan
Long Island - Arizona
GOLD COAST / MELBOURNE
New Orleans, LA
New York, New York
nan
Buffalo, New York
ÜT: 41.033836,-73.775829
Beverly Hills, CA
New Orleans, LA
Long Island - Arizona
Long Island - Arizona
New Orleans, LA
nan
New York
Nashville, TN
New Orleans, LA
New York City
New York City
Boston
austin
New York City, NY
scarborough ontario canada
nan
New York, NY
New York, New York
nan
New York City, NY
New York, NY
New York, New York
New York City, NY
Boston
New York, sort of!
austin
austin
nan
nan
New York City
LA CA USA
New York, New York
austin
• PSU '19 • faith •
New York
nan
New York, New York
nan
nan
austin
nan
Naples, FL, Boston, MA
• PSU '19 • faith •
nan
The Old Dominion
nan
Florham Park, NJ
nan
New York
nan
Florham Park, NJ
New York, New York
Larchmont, New York
nan
Long Island - Arizona
nan
New York City
Naples, FL, Boston, MA
Naples, FL, Boston, MA
nan
Long Island - Arizona
New York, NY
New York, NY
Naples, FL, Boston, MA
Naples, FL, Boston, MA
Washington D.C.
Naples, FL, Boston, MA
Washington D.C.
nan
Naples, FL, Boston, MA
New York, NY
nan
Long Island - Arizona
Washington D.C.
New York, NY
nan
New York, NY
nan
Southbury, CT
Naples, FL, Boston, MA
nan
nan
Brooklyn, NY
New York City
Brooklyn, NY
Washington DC
Buffalo
Malden, MA
Connecticut
brooklyn
Malden, MA
nan
Larchmont, New York
brooklyn
Connecticut
nan
New York, NY
New York, New York
nan
New York, NY
The Old Dominion
New York City
nan
New York City
New York, NY
Larchmont, New York
nan
nan
nan
nan
Grimsby - UK
New York City
nan
New York City
nan
Larchmont, New York
west hartford, connecticut
nan
nan
Merrick
nan
New York, NY
nan
nan
nan
London/NYC
London/NYC
nan
nan
nan
nan
nan
NYC
nan
Austin, TX
Austin, TX
Larchmont, New York
new york city
new york city
new york city
new york city
Dayton, Ohio
New York
New York
RTR
New York
nan
Manhattan, NY
New York City
Manhattan, NY
Washington, DC.
nan
Washington DC Metro
New York
New York
East Coast
UAE
BB PIN:28AF78F4
BB PIN:28AF78F4
BB PIN:28AF78F4
NY
nan
nan
nan
nan
Global
From the South to 'THE' South
Lynn, MA
New York
New York
new york
new york
nan
New York City
nan
ÜT: 42.487548,-71.103035
nan
nan
wherever
nan
New York City
new york
nan
nan
nan
SOUL CLAP/ STAR TIME/BBE
Easton,Ma
nan
nan
Sands Point, NY
SOUL CLAP/ STAR TIME/BBE
SOUL CLAP/ STAR TIME/BBE
nan
Washington, DC
Sands Point, NY
D.C
nan
ÜT: 33.724561,-84.565845
ÜT: 33.724561,-84.565845
Wellesley, MA
nan
New Jersey
New York City, NY
Massachusetts
Miami/Ft. Lauderdale, FL
Southern California
nan
Massachusetts
New Jersey
sweatertown, new england
New Jersey
nan
NY
Rochester, NY
sweatertown, new england
nan
Rochester, NY
San Diego
Rochester, NY
New York City
Brooklyn
Brooklyn
Brooklyn
Toronto, Canada
Brooklyn
metro Boston
Long Island/Westchester
Boston
USA
Boston
nan
Boston
Wellesley, MA
South Australia
nan
nan
nan
nan
By mile 3 of the NYC Marathon
Wellesley, MA
nan
By mile 3 of the NYC Marathon
nan
nan
nan
nan
nan
nan
Grimsby - UK
Long Island, New York
USA
nan
nan
Harvard Professor & MD
Miami/Ft. Lauderdale, FL
Harvard Professor & MD
Harvard Professor & MD
nan
nan
nan
ÜT: 41.538301,-74.072016
Brooklyn, New York
nan
Washington, DC
nan
nan
Washington, DC
nan
Washington, DC
nan
nan
New York, sort of!
New York, sort of!
nan
nan
Provincetown Ma
Washington, DC
40.741909,-73.997189
40.741909,-73.997189
Austin Texas
Stamford, CT
40.741909,-73.997189
nan
Mannahatta
Long Island/Westchester
Washington, DC
Washington, DC
Austin Texas
JetBlue T5 at JFK
Providence, RI
Providence, RI
Virginia & Cali
Louie's in the Bronx
nan
nan
nan
Virginia & Cali
Providence, RI
Mannahatta
nan
Virginia & Cali
nan
Providence, RI
Virginia & Cali
nan
Providence, RI
NYC✈️MIA
Providence, RI
Providence, RI
Provincetown Ma
Providence, RI
Mannahatta
Mannahatta
Providence, RI
Providence, RI
Providence, RI
NYC
Fullerton, CA
nan
ÜT: 27.947395,-82.215546
nan
Fullerton, CA
Costa Rica
CONCRETE JUNGLE
nan
nan
nan
nan
nan
nan
nan
CONCRETE JUNGLE
CONCRETE JUNGLE
CONCRETE JUNGLE
CONCRETE JUNGLE
CONCRETE JUNGLE
Coming to a City Near You!
face, space
Queens, New York
Virginia Beach, VA
Logan International Airport
Virginia Beach, VA
New York
nan
Virginia Beach, VA
nan
new york.
DC
DC
USA
LA & Boston
nan
nan
boise, idaho
boise, idaho
boise, idaho
USA
nan
Cambridge, MA
USA
Sub to me!
USA
Washington DC
New York
Troy, NY
nan
NYC
nan
nan
Cambridge, MA
#theBronx
Albany,NY
nan
USA
New York, NY
Worldwide
Worldwide
Salem, NH
nan
ravioli
ravioli
New York
USA
Orleans/Tarpon Springs/London
Orleans/Tarpon Springs/London
USA
New York, NY | Orlando, FL
New York, NY
New York, NY
New York, NY
Albuquerque, NM
Boston
Boston
Boston
Central Perk
nan
nan
New York
ÜT: 17.9889591,-76.7712636
Central Perk
ÜT: 17.9889591,-76.7712636
Long Beach
Where my pockets can take me
Brooklyn, NY
Ft. Lauderdale
Ft. Lauderdale
New York, NY
Brooklyn, NY
USA
Bushwood
Bushwood
new york
Central Perk
Central Perk
Central Perk
Central Perk
new york
Long Beach
Brooklyn!
new york
nan
nan
Boston, MA
Boston, MA
As global as possible
Boston, MA
Boston, MA
As global as possible
Upstate New York
new york
Ft. Lauderdale
The World
Boston, MA
Ft. Lauderdale
nan
nan
Logan International Airport
Earth
.
New York, NY
los angeles, ca
Hastings on Hudson, NY
USA
Boston, MA
New York City
Boston, MA
Fort Lauderdale, FL
Hastings on Hudson, NY
Hastings on Hudson, NY
Boston, MA
nan
New York City
nan
USA
Here & There
planet earth
Leighton Buzzard
Here & There
planet earth
Boston MA USA
planet earth
Leighton Buzzard
Leighton Buzzard
Leighton Buzzard
Bad Wolf Bay
The City of New York
South jamaica.. Queens
South jamaica.. Queens
South jamaica.. Queens
shopping. anywhere
South jamaica.. Queens
New York City
SJ-BOS-SJ
South jamaica.. Queens
Suburb of Boston, MA
New York City
New York City
Miami, FL
Boston
nan
Boston
nan
planet earth
SJ-BOS-SJ
Bad Wolf Bay
planet earth
Oregon
shopping. anywhere
nan
nan
brooklyn
New York, New York
New York, NY
San Francisco, CA
San Francisco, CA
Bay Area, CA.
Suburb of Boston, MA
Tampa FL & Hendersonville NC
nan
Hong Kong, SAR
Buffalo, New York
New York-Florida
nan
Boston
New York
New York
Bay Area, CA.
nan
Brooklyn, NY
Hoboken,NJ
key west to bar harbor
Bay Area, CA.
nan
Brooklyn, NY
Buffalo, New York, Florida
Brooklyn, NY
Bay Area, CA.
Brooklyn, NY
NYC
New York City
nan
New York City
Global
nan
New York, NY
Global
New York, Tel Aviv
New York, NY
New York, NY
so cal
nan
New York, NY
so cal
nan
New York, NY
FL350
johnson city tn
so cal
nan
Philadelphia
johnson city tn
West Berlin, NJ
JAMAICA&USA
UK
Rochester , N.Y
Boston, MA
Brooklyn, NY
Arlington, VA
San Francisco
nan
Austin, TX/NY, NY
Fort Lauderdale, Fl
Arlington, VA
West Berlin, NJ
Boston, USA
The Eastside of the Far Side
Arlington, VA
nan
Australia
NY
nan
nan
nan
UK
iPhone: 28.356075,-81.588827
Hoboken, NJ
New York, NY
Miami,Florida
Miami,Florida
nan
New York, NY
Hoboken, NJ
New York
nan
New York
nan
nyc
New York, USA
New York, NY
Orlando
nyc
New York, NY
Washington, DC
Cambridge, MA
New York
New York, USA
Logan International Airport
USA
New York
New York
Washington, DC
nan
New York, NY
nan
nan
East Greenwich,RI
Germany
nan
USA
New York
New York, NY
New York, NY
Germany
New York, NY
New York City, NY
New York City, NY
nan
The Eastside of the Far Side
nan
New York City, NY
Orlando - ΜΣΥ
East Greenwich,RI
UK
nan
New York
NY
New York
The Eastside of the Far Side
USA
The Eastside of the Far Side
nan
Quincy, Ma by way of The #413
nan
The Eastside of the Far Side
nan
The Eastside of the Far Side
Quincy, Ma by way of The #413
Quincy, Ma by way of The #413
USA
New York, NY
USA
nan
UK
North Shore Massachusetts
USA
nan
New York, NY
nan
USA
nan
nan
nan
BrendaPriddyAndCompany.com
Tempe, AZ
Tempe, AZ
nan
USA
Salt Lake City, Utah
Maine
nan
nan
nan
Maine
Maine
Maine
nan
San Francisco, CA
manhattan.
manhattan.
USA
San Francisco, CA
nan
nan
The Big Manzana
nan
nan
nan
nan
nan
nan
nan
San Diego, CA (760)
Boston, MA
nan
nan
nan
nan
nan
Boston, MA
nan
Brooklyn, NY
Upstate New York
Dubai
Dubai
Dubai
nan
Boston, MA
Buffalo
Buffalo
Buffalo
Buffalo
NY
NY
Buffalo
Buffalo
nan
Austin, TX
Hollywood
New York
Austin, TX
10 ring
New York
Boston, MA
10 ring
1-800-JETBLUE
Manchester,CT
10 ring
Across America
10 ring
10 ring
Montreal
Jamestown Virginia
✈️FL/NJ/NYC
✈️FL/NJ/NYC
Jamestown Virginia
Miami | Puerto Rico
Miami | Puerto Rico
✈️FL/NJ/NYC
Miami | Puerto Rico
MA
MA
nan
nan
nan
nan
USA
nan
Northern California/Medellín
Dallas, TX
Northern California/Medellín
Boston
USA
NYC
nan
nan
Roslyn, ny
NYC
nan
nan
NYC
nan
Buffalo, NY
nan
nan
nan
nan
nan
nan
nan
nan
Tonawanda NY
nan
MA & NJ
Boston College
USA
Tonawanda NY
nan
nan
nan
South Park, Pa
Burlington, MA
nan
Burlington, MA
NOVA
Naples, Fl
San Francisco
New Jet City
nan
nan
New Jet City
MiamiKlko
Logan.Utah
NY NY
nan
Boston College
nan
nan
Everywhere
Logan.Utah
Boston College
Plymouth, MA
Plymouth, MA
Logan.Utah
Logan International Airport
Logan International Airport
Logan International Airport
Logan International Airport
ÜT: 48.837756,2.320214
NY
NYC
USA
South Florida
NY
NOVA
South Florida
NY
USA
Boston, MA
Boston, MA
D.C.
Boston, MA
Richmond BC
nan
nan
nan
nan
South Florida
nan
nan
nan
nan
nan
Alexandria, VA
nan
nan
nan
nan
Los Angeles, CA
Los Angeles, CA
Massachusetts
Los Angeles, CA
Los Angeles, CA
nan
NYC
NY NY
Richmond BC
Richmond BC
nan
NY NY
✖️ || 4/5 || ✖️
nan
Metro Washington DC
Miami, Florida
Los Angeles, CA
Amherst, NY
Amherst, NY
Amherst, NY
New York City
Alexandria, VA
Alexandria, VA
Upstate New York
Alexandria, VA
New Jersey
Alexandria, VA
Long Island, NY
Chasing the dream
New England
Long Island, NY
Chasing the dream
Washington, DC
Long Island, NY
Washington, DC
nan
Live Free or Die
nan
Waltham, MA
Long Island, NY
nan
johnson city tn
nan
Washington, DC
Washington, D.C.
NYC
nan
Brooklyn, NY
nan
nan
nan
Washington, DC
New York, NY
New York, NY
nan
Where the food at
New York, NY
New York, NY
nan
nan
Washington, DC
nan
Washington, DC
nan
nan
Massachusetts
Long Island, NY
Washington, DC
nan
Washington, DC
nan
Long Island, NY
Salt Lake City, Utah
nan
nan
Queens, NY
nan
nan
Queens, NY
nan
Austin, Texas
Logan International Airport
nan
nan
nan
nan
Easton, pa
nan
Boston
nan
nan
Marblehead, MA
Bay Shore NY
Boston
29 Sawyer Rd., Waltham, MA
nan
South Florida
nan
nan
nan
USA
the friendly skies
London
USA
USA
USA
USA
NBMA
NBMA
Hollywood
Inquiries: CAA • Miller PR
Boston, MA
LA & Boston
gulf coast, FL
Boston, MA
Boston, MA
Inquiries: CAA • Miller PR
Boston, MA
On the road to paradise
On the road to paradise
Boston, Massachusetts
Boston, MA
Great Lakes
Saint Leo
im 5sos af :)))
im 5sos af :)))
im 5sos af :)))
im 5sos af :)))
Waltham, MA
nan
Waltham, MA
nan
Waltham, MA
virginia
nan
Saint Leo
Saint Leo
nan
nan
New York
san francisco CA
nan
nan
ÜT: 40.96513,-73.872957
New York, New York
New York, New York
#ManorvilleInExile
#ManorvilleInExile
nan
02861
New York, New York
USA
nan
nan
Evanston Illinois
nan
#ManorvilleInExile
#ManorvilleInExile
#ManorvilleInExile
nan
nan
Inquiries: CAA • Miller PR
Inquiries: CAA • Miller PR
nan
LA & Boston
nan
nan
nan
Dominican Republic
nan
nan
nan
nan
Austin, TX
nan
Boston, MA
Southborough, Massachusetts
Northern Virginia
Boston, MA
nan
Northern Virginia
Northern Virginia
nan
ÜT: 40.96513,-73.872957
USA
Austin, TX
College Park, MD
Massachusetts
Denver, CO/NYC
Austin, TX
nan
Massachusetts
Austin, TX
Massachusetts
Forest Hills, NY
Los Angeles
nan
USA
nan
Washingon, DC
New York & LA
Forest Hills, NY
nan
nan
nan
ÜT: 40.96513,-73.872957
ÜT: 40.96513,-73.872957
South Miami Heights, Florida
ÜT: 40.96513,-73.872957
ÜT: 40.96513,-73.872957
South Miami Heights, Florida
Wirral England
ÜT: 40.96513,-73.872957
USA
Washington, DC
ÜT: 40.96513,-73.872957
Washington, DC
Washington DC
nan
PHL
nan
Austin, Texas
NBMA
New York
Out and About - Homebase is MD
Boston
NY
USA
nan
Boston
Logan International Airport
nan
nan
NY
USA
NY
NY
Logan International Airport
Logan International Airport
Atlantic City
Logan International Airport
Logan International Airport
Logan International Airport
Canton, MA
Inquiries: CAA • Miller PR
Atlantic City
New York City
Canton, MA
New York City
New York City
Inquiries: CAA • Miller PR
Inquiries: CAA • Miller PR
Alexandria, Va
Inquiries: CAA • Miller PR
USA
ft Lauderdale
pnw
ft Lauderdale
Brooklyn, NY
Boston
Boston
Boston
nan
USA
Liverpool, NY
nan
Boston, MA
Boston, MA
USA
Boston, MA
The Suite Lounge
Buffalo NY/ Pinehurst NC
Buffalo NY/ Pinehurst NC
Buffalo NY/ Pinehurst NC
Buffalo NY/ Pinehurst NC
Buffalo NY/ Pinehurst NC
nan
The Suite Lounge
new orleans
Miami, FL
Mill Creek, WA
Mill Creek, WA
Mill Creek, WA
new orleans
new orleans
Mill Creek, WA
new orleans
USA
right behind you
new orleans
New Orleans, LA
New Orleans, LA
New Orleans, LA
right behind you
San Francisco
USA
San Francisco
nan
nan
nan
nan
nan
nan
San Francisco
New York
Pescadero, CA
nan
nan
iPhone: 33.761105,-118.193817
Miami, FL
Merrick
Manhattan
Boston
BOS via ROC
Newport Beach
right behind you
Stratford, CT
USA
New York
Newport Beach
NYC
nan
nan
NYC | CT
New York City
nan
nan
Florida, USA
USA
NYC
NYC
Parkland Fl
Rhode Island
NYC
NYC
New York
Long Beach, NY
New York
New York, NY
Merrick
San Francisco Bay Area
planet earth
Chicago, an Airplane or Train
Merrick
New York, NY
Merrick
nan
Merrick
nan
nan
Round Rock, TX
nan
Boston, MA
London
London
South West UK
London
nan
nan
Cambridge, MA
nan
Merrick
nan
nan
nan
Merrick
nan
nan
USA
NYC
USA
USA
USA
USA
Los Angeles, CA
NYC
Satellite Beach Fl.
Milton, Massachusetts
BOSTON
BOSTON
BOSTON
BOSTON
BOSTON
nan
USA
New York City, NY
NYC
Provo, UT
Provo, UT
Brooklyn, NY
nan
Provo, UT
nan
nan
nan
San Diego, CA
Provo, UT
nan
USA
nan
USA
nan
nan
nan
nan
Scottsdale
Orange County, USA
New Vernon, NJ
USA
Somewhere in between
nan
New York, NY
nan
nan
Boston
nan
Washington, DC
Boston
Bahhhston.
nan
Boston
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
Greenwich, CT
nan
nan
Greenwich, CT
nan
nan
nan
nan
USA
nan
Long Island, NY
Dorado
nan
nan
New York, New York
Greenwich, CT
New York, New York
Dorado
USA
Greenwich, CT
Greenwich, CT
nan
Dorado
Southbury, CT
Dorado
Dorado
New York, New York
Arlington, Virginia
Merrick
nan
New York, New York
Dorado
nan
Merrick
New York, New York
nan
Merrick
Philadelphia
Englewood, Florida
nan
Washington, D.C.
boston
boston
nan
NYC
NYC
nan
Miami, FL
Boston, MA
NYC
nan
nan
New York, NY
Boston, Massachusetts
nan
Washington, D.C.
CT
New York, NY
❤
❤
plymouth
New York, NY
USA
nan
Jetsetter ✈
New York City
ÜT: 40.96513,-73.872957
Jersey City, NJ
Englewood, Florida
NYC | CT
New York City
NYC | CT
Wayne, NJ
New York City
nan
Wayne, NJ
NYC | CT
nan
Miramar, Florida
ÜT: 40.96513,-73.872957
nan
USA
NY
District of Columbia
San Diego, CA
NYC | CT
Boston, Massachusetts
NY
District of Columbia
NYC | CT
USA
NYC | CT
nan
Florida, USA
Long Island
North Carolina
Queens, NY
nan
nan
nan
Florida, USA
Long Island
Boston
Phoenix AZ
Boston, Ma.
North Carolina
USA
Austin, Texas, USA
nan
New York, NY
pnw
pnw
Clinton, CT
Jersey City
Olean, NY
nan
USA
✈️✈️
iPhone: 60.495510,-151.064590
nan
austin, texas
nan
New York City
Boston, MA
Boston, MA
Boston, MA
nan
New York City
New York City
Portland, OR
USA
Portland, OR
Raleigh, NC
nan
Jamestown Virginia
New York, NY
OH-LA-AZ-NY-AFG-IL-?
nan
New York, NY
Boston, MA
New York, NY
Jersey City, NJ
Jersey City, NJ
New York City
New York City
Melbourne, Australia
Jersey City, NJ
Charlotte, NC
nan
Jersey City, NJ
nan
Chicagoland
Chicagoland
USA
nan
nan
nan
nan
Yauco, Puerto Rico
USA
Phoenix, Az
Puerto Rico
ELL LAY
New Hampshire
Queens, NY
Yauco, Puerto Rico
Yauco, Puerto Rico
NY, NY
Yauco, Puerto Rico
UK
San Francisco
nan
Seattle
Yauco, Puerto Rico
NY, NY
nan
Yauco, Puerto Rico
NYC | NOLA
Old Greenwich, CT
california ☀️
NY
nan
nan
Logan International Airport
nan
UK
nan
Logan International Airport
New York
Boston, MA
nan
New York
USA
nan
Puerto Rico
USA
nan
nan
Colombia
nan
Savannah, GA
UK
undecided
USA
nan
nan
Missourah
nan
nan
nan
nan
nan
nan
nan
Astoria, NY
nan
Paper Street
Raleigh, NC
nan
Astoria, NY
nan
nan
nan
Fort Mill, SC
nan
nan
✈️ Birmingham ✈️ Brooklyn ✈️
nan
nan
San Diego
nan
Boynton Beach, FL
nan
Facebook: BÚFALOS F.C.
Charleston/Houston
nan
1/1 loner squad
nan
nan
Denver, CO
nan
Boynton Beach, FL
Wherever Travel Team C sent me
nan
nan
Saratoga Springs
Washington, DC
nan
nan
nan
Washington, DC
nan
nan
nan
Triad
Austin, tx
nan
nan
nan
Saratoga Springs
Cleveland
Saratoga Springs
Gulfport, MS
Astoria, OR
nan
Just outside of Thunder Dome
nan
Denver, CO
nan
Tidewater - Virginia
nan
Denver, CO
Philadelphia, PA
nan
Philadelphia, PA
nan
Saratoga Springs
Saratoga Springs
nan
Dumfries, Virginia
Just outside of Thunder Dome
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
Barcelona
Brooklyn
nan
nan
New York, NY
nan
nan
In a galaxy far, far away ✨
Tampa, Florida
Minneapolis, MN
nan
Tampa, Florida
nan
Kentucky
Kentucky
Minneapolis, MN
Kentucky
Dumfries, Virginia
Dumfries, Virginia
Boston, MA
nan
Hanover Twp, PA
nan
nan
Boston, MA
nan
Kentucky
Central, New Jersey
nan
USA
Atlanta, United States
Central, New Jersey
Durham, NC
Tampa, Florida
YouTube.com/VOiceofBrian
Kentucky
St. Louis
Hudson Valley, NY
DC
nan
nan
Jacksonville, FL
nan
nan
nan
Jacksonville, FL
nan
nan
Maryland
#yeahTHATgreenville SC
Washington, DC
nan
nan
NYC
East Coast/New England
nan
nan
Miami Beach, Florida USA
nan
MN
Greenville, SC
Greenville, SC
Greenville, SC
Greenville, SC
nan
Greenville, SC
nan
millville,nj
Carmen SanDiego
nan
nan
nan
nan
Richmond, VA
Charlotte, NC
nan
nan
Cleveland, Ohio
nan
Crofton, Maryland
Kentucky
Kentucky
Kentucky
Kentucky
Durham, NC
Durham, NC
Oswego, NY
DC
Manchester
Vancouver, Canada
Stafford VA
nan
nan
Pennsylvania
nan
nan
Washington,D.C.
nan
Pennsylvania
nan
nan
nan
nan
nan
cheektowaga, ny
Porkhampton, England
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
Pennsylvania
nan
nan
nan
nan
Dallas, TX
Pennsylvania
Cleveland, Ohio
nan
New Orleans, LA
nan
Maryland
Maryland
Knoxville
Denver, CO
Maryland
nan
nan
nan
Los Angeles, CA
nan
nan
nan
nan
nan
nan
nan
Maryland
nan
Austin, TX | Atlanta, GA
nan
nan
Denver, NC
nan
Irvine, Ca
nan
nan
Los Angeles
nan
St. Louis to Parris Island, SC
nan
Austin, TX | Atlanta, GA
nan
nan
nan
Southeastern United States
Keene, NH
Crofton, Maryland
nan
nan
Knoxville, TN
USA
nan
Irvine, Ca
nan
Austin, TX | Atlanta, GA
Los Angeles
Austin, TX | Atlanta, GA
nan
Bedford, NH
Columbia, South Carolina
ÜT: 38.8676126,-77.0831512
Knoxville
nan
nan
nan
nan
nan
Los Angeles, CA
Crofton, Maryland
New Orleans, LA
Earth
Charleston, SC and the Earth
Rochester, NY
nan
Las Vegas, NV
Southeastern United States
nan
USA
nan
nan
Florida Raised, NYC Based
nan
BTR/DCA/IAD/MSY - etc
nan
South Jersey
BTR/DCA/IAD/MSY - etc
nan
Bedford, NH
Las Vegas, NV
nan
BTR/DCA/IAD/MSY - etc
BTR/DCA/IAD/MSY - etc
NY
New Mexico
BTR/DCA/IAD/MSY - etc
Las Vegas, NV
nan
nan
BTR/DCA/IAD/MSY - etc
nan
USA
citizen of the world
nan
Rhode Island
philadephia, pa
nan
nan
San Jose, Ca
nan
BTR/DCA/IAD/MSY - etc
nan
NY
Las Vegas, NV
nan
Washington, DC
nan
nan
nan
nan
at the rink
BTR/DCA/IAD/MSY - etc
nan
BTR/DCA/IAD/MSY - etc
BTR/DCA/IAD/MSY - etc
Denver, CO
BTR/DCA/IAD/MSY - etc
nan
Miami, Florida, USA
Florida Raised, NYC Based
Florida Raised, NYC Based
Florida Raised, NYC Based
Philadelphia, PA
Philadelphia, PA USA
nan
Miami, Florida, USA
San Jose, Ca
Gold Coast, Australia
Manchester
San Jose, Ca
Florida Raised, NYC Based
Florida Raised, NYC Based
The Shire
South
BTR/DCA/IAD/MSY - etc
NY
NY
NYC
nan
BTR/DCA/IAD/MSY - etc
Does it really matter
Brooklyn, NY
nan
Washington D.C.
TLH
nan
Miami, Florida, USA
New Haven, CT
Brooklyn, NY
Washington D.C.
BTR/DCA/IAD/MSY - etc
nan
Washington D.C.
Washington D.C.
Spokane, Washington
Washington D.C.
Cali native now in Charlotte
BTR/DCA/IAD/MSY - etc
nan
nan
nan
Cali native now in Charlotte
Miami, Florida, USA
nan
Does it really matter
South
Does it really matter
nan
nan
nan
nan
VT
Does it really matter
nan
nan
nan
nan
Carmen SanDiego
Does it really matter
Indianapolis
nan
nan
nan
The Shire
nan
mclean, va
Does it really matter
nan
London UK & USA
Does it really matter
Does it really matter
nan
nan
nan
YouTube.com/VOiceofBrian
nan
nan
mclean, va
nan
VT
Does it really matter
The Shire
nan
Arlington, VA
Does it really matter
Does it really matter
Philadelphia, PA
nan
North Carolina
Washington D.C.
nan
nan
nan
nan
ÜT: 42.076557,-76.770488
nan
Portland, Maine
nan
nan
The Shire
nan
nan
nan
nan
nan
VA
Northern Virginia
Philadelphia, PA
nan
nan
nan
nan
nan
Wherever my breath takes me
nan
nan
nan
nan
TotalWorldJaxxination
nan
nan
nan
Baltimore, MD
Arlington, VA
Washington D.C.
nan
nan
nan
nan
@Mister617 follows you
nan
Shawano WI
nan
nan
Los Angeles, CA
nan
Spokane, Washington
Spokane, Washington
New Mexico
New Mexico
Washington D.C.
Arlington, VA
Houston TX
Wherever my breath takes me
nan
nan
nan
Washington D.C.
nan
Arlington, VA
Aldie va
TotalWorldJaxxination
Arlington, VA
nan
wilmington, nc
nan
nan
nan
nan
Watching @Interpol somewhere
Watching @Interpol somewhere
Washington, DC
nan
South
nan
nan
nan
nan
nan
nan
nan
New Orleans
Pittsburgh, PA
NYC // LI
South
Washington DC
Philadelphia
Stillwater, MN
Stillwater, MN
Indianapolis
nan
Cincinnati, OH
nan
39.0708° N, 106.9886° W
Washington DC
New York, NY
nan
Watching @Interpol somewhere
Dallas, TX
Shawano WI
Washington, DC
Pennsylvania
Melbourne, Australia
BK
Jacksonville
South
New Haven, CT
Pennsylvania
Washington, DC
Carmel, IN
Dallas, TX
Mentor/Columbus
nan
nan
Watching @Interpol somewhere
Watching @Interpol somewhere
Danmark
Jacksonville
Richmond, VA
Carlsbad, CA
Washington, DC
Colorado
South
nan
Hillsborough, New Jersey
Dallas, TX
go central
Wayland, MA USA
Mentor/Columbus
TLH
Washington, DC
Washington, DC
east coast
nan
Sheffield
Stillwater, MN
Stillwater, MN
New Haven, CT
nan
Washington, DC
Baltimore, MD
NYC // LI
nan
nan
Boston, MA
Hillsborough, New Jersey
Richmond, VA
BK
BK
TLH
babynole'18
Des Moines, IA
BK
Newark, DE
nan
BK
BK
Stafford VA
Sheffield
nan
nan
Boston, MA
BK
BK
nan
BK
nan
nan
nan
nan
Terrace Park OH
NYC // LI
Des Moines, IA
nan
Boston, MA
Southeastern Pennsylvania USA
nan
nan
Southeastern Pennsylvania USA
nan
nan
nan
nan
Southeastern Pennsylvania USA
nan
nan
Boston, MA
Washington DC
Boston, MA
BK
Dallas, TX
ATL | DTW | NRT
earth for now
Boston, MA
Boston, MA
Mobile, Alabama, US
nan
nan
nan
Boston, MA
nan
nan
Nube de la Reign
Jacksonville
Your Dreams
New York
nan
nan
Sacramento, CA 95864
Jacksonville
Jacksonville
Nebraska
Boston, MA
Jacksonville
los angeles ca
The Specific Ocean
Boston, MA
NC, USA
nan
NC, USA
nan
Miami, Fl. USA
Sherman Oaks, CA
North Carolina
Scituate, MA
nan
nan
nan
nan
Boston
London
Edmonton
Great Barrington, MA
Russia, Россия
Miami, Fl. USA
Washington DC
Blackwood, NJ
nan
nan
Washington, DC
South
Washington, D.C.
nan
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington D.C.
Washington, D.C.
Washington, D.C.
nan
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Washington, D.C.
Conway, AR
Adelaide, South Australia
Detroit - Hampton U.
Detroit - Hampton U.
nan
nan
nan
Michigan ➡️ Florida
nan
ImInMiamiBitch
nan
nan
nan
RDU
Michigan ➡️ Florida
Philly Yo
LS ❤️
nan
nan
Down The Rabbit Hole
Wayne,PA USA
Austin TX
nan
Louisiana
Sandy Eggo, California
Pennsylvania
nan
Pennsylvania
Phoenixville, Pa
nan
Pennsylvania
Philadelphia, PA
Philadelphia, PA
Philadelphia, PA
Rhode Island
Philadelphia, PA
Philadelphia, PA
University of Vermont
Philadelphia, PA
The Last Floater
Philly
nan
Washington DC
nan
Nebraska
Washington, D.C.
nan
Philly
nan
Washington, D.C.
NY - SoFlo - The Tardis
nan
WITSEC League
New Jersey
nan
Columbus, Ohio
Santa Cruz, CA
Southeastern Pennsylvania USA
Southeastern Pennsylvania USA
ODU swim and dive '17
Southeastern Pennsylvania USA
Southeastern Pennsylvania USA
Michigan ➡️ Florida
Michigan ➡️ Florida
Southeastern Pennsylvania USA
nan
nan
nan
nan
Does it really matter
nan
New Haven, CT
nan
nan
nan
Celina, TX
Washington, DC
nan
nan
nan
nan
Does it really matter
nan
nan
Raleigh, NC
North Carolina
Englewood, Florida
Boston, MA
Raleigh, NC
Earth
Wherever US Air takes me...
nan
nan
Does it really matter
nan
Spring HIll, FL
Indianapolis
nan
Boston
nan
Earth
USA
nan
nan
WITSEC League
nan
nan
nan
nan
DC|LA
Washington, D.C.
Philly Yo
Tuscaloosa, Alabama
New River AZ
Belle MO
Thunder Bay, ON, Canada
Belle MO
New York
Tampa
Belle MO
New York
Belle MO
Chicago, IL
Chicago, IL, USA
Belle MO
ÜT: 42.076557,-76.770488
Miami Beach, Florida
Belle MO
MA, NYC
nan
Chicago, IL, USA
Nebraska
Alexandria, VA
nan
stl
nan
nan
Nebraska
nan
Indiana
ÜT: 42.076557,-76.770488
Does it really matter
Does it really matter
Philadelphia
nan
Does it really matter
San Diego
Does it really matter
Does it really matter
Raleigh, NC
New York, New York
nan
Philly Area
Nebraska
South
nan
WITSEC League
Pony Paradise, VA
nan
Philly, Chicago, MSP, Vegas
O-H
Washington, DC
ÜT: 42.076557,-76.770488
H-Town Houston, Texas
San Francisco, CA
ÜT: 42.076557,-76.770488
nan
ÜT: 42.076557,-76.770488
Boston, MA
nan
united states georgia
Jerz
nan
nan
Philadelphia
nan
nan
nan
Beaufort, SC
San Francisco, CA
Pony Paradise, VA
Chicago, IL
Virginia, USA
nan
Jerz
Indiana!
SC
Indiana!
St. Augustine, Florida
Sacramento, CA 95864
West Chester, Pa
nan
Atl
nan
nan
St. Augustine, Florida
St. Augustine, Florida
SC
San Francisco
nan
nan
nan
nan
Charlotte, NorCack
south beach/ LA / NC
Bavaria, Germany
nan
Indiana!
nan
Bavaria, Germany
nan
nan
Boston area
Citizen of the World
nan
Atascadero, CA
nan
Tuscaloosa, Alabama
nan
West Chester, PA
nan
Charleston, SC
Thunder Bay, ON, Canada
nan
nan
nan
Sunny mountains of Arizona
nan
Elmwood Park
Phoenix, AZ
The Great Loop
Washington DC
Tampa
Washington, D.C.
nan
nan
Livonia, MI
Livonia, MI
Livonia, MI
Washington, D.C.
nan
nan
Livonia, MI
NY
NYC
Los Angeles, CA
nan
Lewes, DE, USA
Washington, D.C.
nan
Washington, D.C.
nan
Washington, D.C.
Stillwater, MN
nan
East Lansing
G-Town, Geneva NY
Goldsboro, NC
Boston area
Goldsboro, NC
nan
San Francisco
Los Angeles, CA
Washington, D.C.
nan
nan
nan
nan
nan
nan
Philly
Boston, MA
Philly
nan
nan
nan
Bonaire, GA
nan
Charleston, SC
nan
nan
Los Angeles, CA
A galaxy far far away
Kingston ontario Canada
A galaxy far far away
A galaxy far far away
Mount Airy, NC
nan
World Wide
everywhere, all the time.
Chicago, IL
World Wide
27265
Windermere, Florida
nan
WCHOB, Buffalo, NY
nan
nan
nan
Inter/Outer-Continental U.S.
Philadelphia
Inter/Outer-Continental U.S.
Columbus, Ohio
VA
Saint Louis, MO
The Wilming Town
nan
nan
nan
nan
Los Angeles
Columbus, Ohio
The Wilming Town
Columbus, Ohio
nan
Madbury, NH
nan
Los Angeles, Ca
nan
On a beach
Global Citizen
Wauwatosa, WI
Orange, CA
Leesburg, VA
Winston Salem, NC, USA
nan
United States
nan
Carmel, IN
nan
Chicago, IL
Newtonville, MA
nan
USA
Wonderland
Chicago, IL
Where you least need me to be
nan
nan
Pittsfield, MA
The 27th State
Chicago, IL
Airports Around The World
nan
nan
Where you least need me to be
Madbury, NH
Where you least need me to be
KENTUCKINMYSHIRT
New York
New York
michigan
PR: 1stlady@theimagecartel.com
Charleston, SC
PR: 1stlady@theimagecartel.com
brown deer, wi
Westeros
20001
Philly to NY/NJ
colorado
Boston
Greensboro NC
Boston
Dallas, TX
nan
Dallas, Burlington, Boston
Boston
Dallas, Burlington, Boston
nan
nan
violet crown
nan
Lexington, KY
nan
Dallas, Burlington, Boston
Carlsbad, CA
nan
Durham, NC
East Lansing
washington dc
♑ NYC
Pittsburgh, Pa
nan
Washington DC
nan
San Francisco, CA
nan
nan
Madbury, NH
39.0708° N, 106.9886° W
babynole'18
nan
Cape Cod
Boston, MA
Newark, DE
Washington, DC
Tipperary, Ireland
Syracuse
39.0708° N, 106.9886° W
nan
nan
nan
New York City
michigan
World
washington dc
nan
Indiana Girl
nan
nan
Newark, DE
Washington, DC
Syracuse
nan
nan
nan
20001
Baltimore, MD
USA
nan
nan
Philadelphia, PA
Philadelphia, PA
Romansville, PA
ÜT: 34.120264,-80.900129
LOS ANGELES
PA
nan
Washington, D.C.
nan
Dallas, Burlington, Boston
Wash DC Metro
Washington, D.C.
nan
nan
nan
Durham, NC
Washington, DC
nan
Boston, MA
Cape Cod
nan
Franklin, TN
Bruxelle-Paris-Marseille-(BPM)
nan
America
SWFL
A galaxy far far away
Washington, DC
Washington, DC
nan
Doylestown, PA
nan
colorado
Alabama
Blackpool
Wonderland
Minnesota, USA
Blackpool
kansas city, mo
kansas city, mo
Great Barrington, MA
Phoenixville, PA
colorado
nan
nan
nan
Pennsauken, NJ
Doylestown, PA
nan
nan
England
75 Drayton Park, London, UK
nan
nan
A galaxy far far away
Washington DC
Bruxelle-Paris-Marseille-(BPM)
Arlington, VA
nan
Washington DC
Pittsburgh, Pa
nan
Bruxelle-Paris-Marseille-(BPM)
Thicktown, Thickania
Elizabeth, PA
Bruxelle-Paris-Marseille-(BPM)
Washington, DC
Bruxelle-Paris-Marseille-(BPM)
A galaxy far far away
A galaxy far far away
Franklin, TN
nan
Alabama
Great Barrington, MA
Great Barrington, MA
nan
Where you least need me to be
Phila, Princeton, NYC.
Washington, DC
Doylestown, PA
Phoenixville, PA
Cincinnati, OH
nan
Washington, D.C.
nan
nan
nan
The Land of Twitter
Unley Oval, Norwood SA
south beach/ LA / NC
nan
Winston-Salem, NC
Cincinnati, OH
south beach/ LA / NC
south beach/ LA / NC
south beach/ LA / NC
Washington, DC
Philadelphia
nan
Charlotte, NC
scrummy hooker
America
US of A
America
US of A
20001
nan
nan
nan
nan
nan
nan
Blue Ridge High School
Columbus, OH
VT
ÜT: 43.182918,-77.651224
nan
San Francisco
Wisconsin
nan
nan
20001
New York State of Mind
nan
nan
Pittsburgh
Leeds, West Yorkshire
wherever uncle sam sends me
Thicktown, Thickania
nan
nan
Arlington, VA
Cambridge, Ma
nan
nan
Doylestown, PA
nan
MA, USA
MA, USA
nan
Columbus, Ohio USA
MA, USA
Belmar, NJ
nan
nan
Doylestown, PA
Pony Paradise, VA
Brooklyn, NY
nan
Old City Philly
nan
Rochester, NY
iPhone: 39.285219,-76.622649
nan
Rochester, NY
nan
nan
nan
nan
Bruxelle-Paris-Marseille-(BPM)
Washington DC
nan
nan
nan
nan
nan
nan
nan
philadelphia, pa
New York
West Virginia
West Virginia
America
iPhone: 36.845329,-75.993790
Charlotte, NC
nan
Robesonia, PA
Des Moines
nan
nan
nan
nan
Thicktown, Thickania
brooklyn
nan
nan
nan
nan
nan
nan
brooklyn
Adelaide, Australia
nan
nan
Boston Area
nan
nan
nan
nan
nan
East Coast City
Little Falls, NJ
nan
nan
Franklin, TN
Bronx, NY
Bronx, NY
Bronx, NY
Brooklyn, NY
New York
nan
nan
Cape Cod
Lewes, DE, USA
Franklin, TN
nan
nan
Philadelphia
http://about.me/miffsc
nan
nan
nan
nan
Washington, DC.
nan
nan
nan
nan
Lewes, DE, USA
United States
Hoover, Alabama
Richmond, VA
nan
Boston, MA
United States
nan
NYC
nan
NYC
nan
NYC
nan
nan
West Virginia
Philadelphia, PA
nan
Philadelphia
39.0708° N, 106.9886° W
Long Island, NY
Bruxelle-Paris-Marseille-(BPM)
nan
Blue Ridge High School
nan
Barneys New York
39.0708° N, 106.9886° W
Lewes, DE, USA
nan
Miami, Florida
Wauwatosa, WI
nan
NYC
nan
Ocean View, Hawaii
Boston, Ma
Blue Ridge High School
nan
nan
nan
nan
nan
nan
39.0708° N, 106.9886° W
Lewes, DE, USA
nan
Atlanta, GA
Lewes, DE, USA
nan
39.0708° N, 106.9886° W
Miami, Florida
nan
nan
39.0708° N, 106.9886° W
nan
nan
Cape Cod
Bruxelle-Paris-Marseille-(BPM)
nan
40.229994,-74.893221
The road to glory
Gloucester, MA
Albany, NY
Gloucester, MA
CT
Blue Ridge High School
East Coast City
nan
NYC
Ocean View, Hawaii
NYC
NYC
nan
NYC
NYC
Levittown, Pa
nan
nan
Virginia
Lewes, DE, USA
Virginia
London
Virginia
New York, NY
Halifax, Nova Scotia, Canada
nan
Virginia Beach, Virginia
Virginia Beach, Virginia
www.bucknellbison.com
nan
nan
Troy, NY
Boston, MA
nan
nan
San Francisco
Boston, MA
nan
nan
This Ain't Chicago, Tennessee
NYC
Denver, CO
nan
nan
nan
nan
nan
Brooklyn
nan
nan
Denver, CO
Denver, CO
nan
Southampton, UK
Mabel, MN
nan
nan
Halifax, Nova Scotia, Canada
Austin, Texas
Nashville, TN
nan
nan
Los Angeles
New York
New York
America
nan
nan
Troy, NY
nan
Sapere Aude
Washington, D.C.
Cleveland, OH
nan
Cleveland, OH
san diego
New York, NY
nan
Southampton, UK
Leeds, West Yorkshire
New York
New York
nan
san diego
Wisconsin, Arizona and...
nan
oakland , ca
Boston, MA
New York
London
New York, NY
Cleveland, OH
nan
nan
nan
nan
Cleveland, OH
nan
Maine
nan
Washington DC
Pennsylvania
Grimsby - UK
connecticut
Where ever God puts me
Charlotte, NC
nan
nan
Los Angeles
cincinnati, ohio
nan
nan
nan
Nwps CA.
nan
San Francisco
33027
nan
nan
Rhos-on-Sea
Flyover country
nan
nan
Flyover country
33027
nan
nan
nan
nan
GLOBAL CITIZEN
Asheville, NC
Kokomo, IN
Knoxville, TN
Asheville, NC
Asheville, NC
Asheville, NC
Asheville, NC
Asheville, NC
nan
Asheville, NC
CT, USA
CT, USA
West Des Moines, IA
CT, USA
CT, USA
Philadelphia, PA
Boston, MA
Charlotte, NC
CT, USA
Arizona
Kokomo, IN
washington, dc
CT, USA
nan
CT, USA
Flyover country
nan
CT, USA
Troy, NY
nan
Downtown Phoenix (#dtphx), AZ
GLOBAL CITIZEN
ÜT: 43.106744,-76.14949
nan
dc
nan
Miami, Fla
nan
charlotte, north carolina
Washington, DC
GLOBAL CITIZEN
nan
Philly
Cleveland/Sandusky
GLOBAL CITIZEN
Washington DC
Washington DC
nan
DC
DC
nan
CT
NYC/Nashville
Earth
San Francisco
the peez
Toronto, ON
Nashville/NYC
Cleveland/Sandusky
Burbank
nan
NYC/Nashville
nan
nan
Bushwood
nan
CT, USA
ÜT: 36.149602,-86.779693
nan
Englewood, Florida
nan
Falls Church, VA
nan
nan
Washington, DC
nan
ÜT: 36.149602,-86.779693
Toronto, ON
nan
New Haven, CT
Buffalo, NY
Maryland
nan
Wexford, PA
nan
Pittsford, Vermont
nan
VA
Sequim, WA
Sequim, WA
nan
nan
Washington, D.C.
nan
Falls Church, VA
nan
nan
nan
Texas
Your Dreams
nan
nan
phoenix, AZ
phoenix, AZ
Pittsford, Vermont
nan
NYC
nan
NYC
NYC
Washington, DC
Boston, MA
Delaware
Nürnberg, Germany
washington, dc
Chesapeake Bay watershed
Chesapeake Bay watershed
New York, NY
Earth
nan
nan
NYC
Nairobi
nan
Brooklyn, NY
Brooklyn, NY
Nairobi
Philadelphia
nan
Asheville, NC
Asheville, NC
nashville via minnesota
Asheville, NC
Nürnberg, Germany
Asheville, NC
nan
nan
Washington, DC
Raxacoricofallapatorius
Raxacoricofallapatorius
Raxacoricofallapatorius
Raxacoricofallapatorius
Raxacoricofallapatorius
nan
nan
nan
New York, NY
NYC
nan
nan
El Paso, TX
NYC
El Paso, TX
Russia
Seattle, WA
Los Angeles by way of Philly
somewhere only we know
Rialto, Ca
nan
Los Angeles by way of Philly
nan
Santa Cruz, CA
Birmingham, AL
Asheville, NC
Asheville, NC
Asheville, NC
nan
Asheville, NC
Asheville, NC
Asheville, NC
Asheville, NC
Asheville, NC
Crystal Lake, IL
Birmingham, AL
nan
nan
nan
Boston, MA
nan
Birmingham, AL
Santa Cruz, CA
nan
nan
nan
Been inside your mum since '92
Crystal Lake, IL
nan
nan
Arizona
Ptown, VA
nan
nan
nan
nan
Knoxville, TN
Rhode Island-Concord, NC-DMV
California
Austin, TX
California
nan
nan
neenah, wi
DC - SF - NC - MA
Charlotte, NC
McKinney TX
McKinney TX
McKinney TX
McKinney TX
McKinney TX
Been inside your mum since '92
nan
McKinney TX
McKinney TX
Knoxville
Columbus, OH
Florida
McKinney TX
Pittsburgh, PA
McKinney TX
nan
Knoxville
nan
nan
nan
Stamford, Ct
Arkansas, USA
Chicago
nan
McKinney TX
Stamford, Ct
Stamford, Ct
Orlando
nan
Independence, MO
nan
Denver, Colorado
Denver, Colorado
Stamford, Ct
San Francisco & Scottsdale
nan
nan
Philadelphia, USA
Callahan, FL
nan
Boston
Cambridge, MA
Independence, MO
nan
Stamford, Ct
Philly
philadelphia
Delaware
Delaware
nan
South Carolina
South Carolina
New York
Delaware
South Carolina
nan
Stratosphere
nan
nan
Jamaica
Jamaica
Atlanta, Ga
nan
Stamford, Ct
nan
Terrapin Station
Stamford, Ct
ÜT: 41.5333,-93.62944
nan
Asheville, NC
Callahan, FL
nan
Delaware
nan
nan
Adelaide, South Australia
Asheville, NC
Stamford, Ct
nan
ÜT: 38.955822,-94.728202
Somewhere in the Milky Way
Stamford, Ct
nan
nan
Asheville, NC
nan
nan
New York, London, Tokyo
nan
nan
Syracuse
nan
Adelaide, South Australia
Orlando
ÜT: 38.955822,-94.728202
Washington, DC via Boston
Arlington Va
nan
New York
nan
London
Boston
Orlando
Boston, MA
nan
Phoenix, AZ
tennessee
Washington, DC via Boston
nan
nan
nan
Boston, MA
nan
iPhone: 38.851982,-77.185593
Washington, DC via Boston
Orlando
nan
Portland, Maine
this must be the place
nan
johnson city tn
johnson city tn
Washington, D.C.
nan
Burbank
nan
Grand Forks, ND
nan
NYC, NY
nan
nan
nan
nan
Sunshine State
nan
nan
johnson city tn
Dallas Fort Worth
Sunshine State
nan
nan
nan
L.A.
nan
nan
nan
nan
Berwyn, PA
Washington, DC via Boston
nan
Washington, DC
nan
nan
Washington, DC
Los Angeles, Ca
nan
nan
nan
Washington, DC
nan
south beach/ LA / NC
PHL | SFO | $x
Charlotte, NC
Washington DC
nan
south beach/ LA / NC
Indianapolis & environs
New York, New York
nan
babynole'18
nan
42.635976,-71.164046
nan
Washington D.C.
nan
nan
nan
Indianapolis & environs
Pittsburgh, PA
nan
Fairfield, Connecticut
Boston,MA
New York, New York
babynole'18
south beach/ LA / NC
nan
42.635976,-71.164046
nan
Jamaica
Boston,MA
Cincinnati
West Palm
West Palm
Delaware
Charlotte, NC
QueenCity, USA
nan
nan
42.635976,-71.164046
nan
Jamaica
Jamaica
nan
nan
nan
nan
Washington, DC, USA
Delaware
Dixie State College
QueenCity, USA
New York, New York
QueenCity, USA
42.635976,-71.164046
San Francisco, CA
Dublin, Ireland
nan
nan
On a beach
QueenCity, USA
nan
Nantucket
Nantucket
West Palm
On a beach
New Hampshire
New Hampshire
West Palm
West Palm
On a beach
On a beach
Flying Giraffe Travel
Wendell, NC
Flying Giraffe Travel
On a beach
On a beach
nan
On a beach
On a beach
On a beach
Haverford, PA & Wilton Manors
Flying Giraffe Travel
On a beach
Haverford, PA & Wilton Manors
On a beach
On a beach
On a beach
GERMANY
Haverford, PA & Wilton Manors
Haverford, PA & Wilton Manors
DC via Louisiana
Haverford, PA & Wilton Manors
Flying Giraffe Travel
Haverford, PA & Wilton Manors
Chicago Area
Knoxville, TN
Washington, DC
Nantucket
nan
Nantucket
Orlando
Alexandria, VA
Nantucket
Nantucket
nan
nan
NJ 973 | NC 910
nan
nan
Puerto Rico
nan
nan
ÜT: 39.855018,-75.360949
The South
nan
DC
42.635976,-71.164046
New York, New York
nan
DC
Williamson West Virginia
DC
DC
DC
DC
nan
DC
nan
Arkansas, USA
nan
11 W Boston St Chandler, AZ
nan
nan
nan
Arkansas, USA
nan
Arkansas, USA
DC
nan
Washington DC
nan
DC
nan
nan
Caywood
Washington DC
nan
nan
nan
nan
Long Island
Columbia, SC
Arkansas, USA
nan
NY
NY
nan
nan
tennessee
nan
nan
nan
nan
nan
nan
nan
Seat 3A, Always
World Traveler
Farmington Hills, MI
Farmington Hills, MI
ÜT: 39.855018,-75.360949
nan
Canada
Austin, TX
Virginia
nan
New York, NY
Sydney boy in Sheffield
Twin Cities
nan
nan
Canada
Canada
On my phone, USA
Austin,TX
Silver Spring, MD
Centreville, VA
Austin, TX
Vienna, VA
On my phone, USA
On my phone, USA
On my phone, USA
Ottawa, Ontario, The Universe
Pittsburgh, PA
nan
nan
ÜT: 41.233943,-74.3966664
nan
Naples, FL
nan
Shenanigan Central
Seattle
nan
Burlington, MA
Madrid, España
nan
DC
nan
nan
nan
Virginia
GERMANY
nan
GERMANY
nan
Phoenix, AZ, USA
Phoenix, AZ, USA
philadelphia, pa
Hershey, PA
Connecticut
Madison, WI
new yorker stranded in PA
Paradise Falls
new yorker stranded in PA
Miami, FL
Midwest
Mexico, D.F.
Monterey County, CA
Miami, FL
nan
Arizona
Arizona
Arizona
Boston, MA
The alternate universe
nan
Bristol, CT
nan
nan
nan
Monterey County, CA
Lincoln, NE
nan
Pune
Home sweet Hollywood Home
Brooklyn, NY
nan
Monterey County, CA
nan
Waynesboro, VA
Monterey County, CA
Belle MO
Waynesboro, VA
nan
Washington, DC
Monterey County, CA
Cary, NC
nan
National, based in Texas
Washington, DC
nan
nan
nan
Manchester
Chicago, IL
nan
New York, NY
Scottsdale, AZ
nan
Monterey County, CA
Liverpool, UK
Scottsdale, AZ
Mount Holly, NC, USA
Home sweet Hollywood Home
nan
nan
nan
Monterey County, CA
nan
nan
Liverpool, UK
Liverpool, UK
nan
Liverpool, UK
nan
Liverpool, UK
Monterey County, CA
Haverford, PA & Wilton Manors
Monterey County, CA
Monterey County, CA
Semiahmoo, WA Soonerland
Monterey County, CA
North Carolina/LosAngeles
nan
Dallas/Fort Worth
Liverpool, UK
nan
nan
Hollywood, CA
Pittsburgh, PA
Harrisburg
nan
County of Kings, NYC
nan
nan
nan
nan
nan
East Coast, US
nan
nan
U.S.
nan
Iowa CIty
Twittersphere
nan
New York, NY | Brooklyn
Tacoma Washington
nan
new yorker stranded in PA
Central Pennsylvania
Jawjah
San Francisco
new yorker stranded in PA
nan
nan
Philadelphia
Old City Philly
ÜT: 39.041902,-77.099677
Denver
Anoka MN
U.S.
nan
nan
nan
nan
nan
Boston, MA
USA
Denver
Denver
nan
nan
Arlington, VA
Boston
nan
Mexico, D.F.
nan
Mexico, D.F.
Washington D.C.
757
Here. There. Everywhere.
Old City Philly
Here. There. Everywhere.
Here. There. Everywhere.
Scruffyville
Scruffyville
West London, UK
nan
Scruffyville
West London, UK
nan
nan
wonderland
Loch nEathach Co Aontroim
40.0587, -75.3659
40.0587, -75.3659
40.0587, -75.3659
nan
Scruffyville
nan
nan
nan
Scruffyville
Old City Philly
nan
nan
nan
nan
Tacoma Washington
Tacoma Washington
Florida
nan
New York/N. Carolina
nan
nan
nan
nan
nan
nan
nan
where ever i lay my hat
nan
Merano, Italy
New York City
nan
nan
Pittsburgh, PA
Memphis, Tennessee
I'm a 917 girl.
ÜT: 41.233943,-74.3966664
Phoenix, AZ
nan
nan
Born in NY, living in PA
nan
nan
San Diego, CA
nan
New York City
Georgia
New York & Worldwide
Phoenix, AZ
Washington, DC
The District of Columbia
San Francisco, CA
Anoka MN
ÜT: 41.233943,-74.3966664
NY/SoCal
nan
Scott Depot, WV
nan
nan
New York City
New York City
Queens, New York
Phoenix, AZ
Dallas, TX
nan
Murfreesboro- Memphis
Wisconsin
Wisconsin
nan
NYC
nan
Wisconsin
nan
Murfreesboro- Memphis
chu
Wisconsin
nan
nan
nan
Puerto Rico
nan
nan
nan
nan
San Francisco, CA
dublin, ohio
Clemson, SC
nan
nan
Phoenix, AZ
nan
nan
nan
Redmond, WA
Redmond, WA
San Francisco, CA
Sistine Chapel
Washington DC
Redmond, WA
nan
ÜT: 41.5333,-93.62944
New York
Redmond, WA
San Francisco, CA
Here
Queens, New York
New York
Redmond, WA
Queens, New York
Dallas, TX
Dallas, TX
New York & Worldwide
Queens, New York
Washington, DC
nan
Philadelphia, PA
nan
Syracuse, New York
Queens, New York
nan
Terrapin Station
Queens, New York
Queens, New York
Kansas, USA
nan
san jose , ca
Connecticut/Vegas
New York
Fort Worth, TX
Proud to say I'm from Dundee
nan
Lawrence, MA
nan
Lawrence, MA
Lowell, Ma
Lawrence, MA
New York
Delray Beach, FL
ÜT: 43.182918,-77.651224
Dallas, TX
US
SBA, BOS, JFK, EWR, SFO, LHR
Dallas, TX
New York, New York
On my phone, USA
ÜT: 42.798909,-71.542817
Lawrence, MA
Lawrence, MA
DC
nan
#Supergabe - Central Maine
nan
ÜT: 42.798909,-71.542817
nan
nan
Raleigh,NC
Winston Salem, NC
ÜT: 42.798909,-71.542817
nan
Charlotte, NC
nan
Mesa, AZ
Charlotte, NC
nan
nan
The Rugby Universe
Boston, MA | San Jose, CA
nan
Charlotte, NC
nan
nan
Boston
nan
Birmingham, AL
Made in China
Chicago, IL
nan
nan
nan
nan
ÜT: 43.182918,-77.651224
nan
nan
nan
Detroit, MI
Dallas, TX
nan
Franklin, TN
Right where I should be!
Detroit, MI
okinawa, japan
nan
nan
Planet Earth
Washington, D.C.
nan
okinawa, japan
okinawa, japan
okinawa, japan
okinawa, japan
Fort Wayne, IN
nan
nan
Greenville, SC
nan
nan
Memphis, Tennessee
Atlanta
US
Phoenix, AZ
Washington, D.C.
nan
USA
Russia, Россия
Earth
nan
nan
Earth
Palm Springs, CA
Earth
Washington, D.C.
Delaware
nan
NY
Earth
nan
Planet Earth
nan
nan
nan
Miami,Fl
nan
Palm Springs, CA
Washington, D.C.
nan
nan
Miami,Fl
nan
Seattle
nan
Wonderland
Fernandina Beach, FL
nan
nan
nan
nan
nan
Charlottesville, VA.
nan
nan
nan
Charlottesville, VA.
CT to Queens
nan
London, ON'
nan
London, ON'
nan
Richmond, Virginia
CT to Queens
nan
Washington, DC
Richmond, Virginia
nan
nan
Fernandina Beach, FL
nan
nan
London
Philadelphia, PA
nan
Melbourne, Australia
nan
Melbourne, Australia
Melbourne, Australia
Russia, Россия
nan
nan
nan
Lowell, Ma
nan
Dallas, TX
NYC/London
NYC/London
nan
nan
NYC/London
Houston Texas
nan
nan
Russia, Россия
Seattle
Major metro area, USA
Lexington
where you want to be.
Houston Texas
where you want to be.
Worthington, Ohio
where you want to be.
South Carolina
nan
where you want to be.
Houston Texas
NJ
Indianapolis, IN, USA
Houston Texas
Washington, D.C.
nan
nan
nan
Any Martin Luther St.
London
NJ
nan
nan
nan
Washington, D.C.
Atlanta, Ga.
Tri-State
nan
Tri-State
On a mountain in Eastern PA
Any Martin Luther St.
washington, dc
nan
washington, dc
Any Martin Luther St.
Any Martin Luther St.
Providence, Rhode Island
Any Martin Luther St.
Russia, Россия
Orlando
Mid Michigan
Mid Michigan
Up In The Air
Up In The Air
nan
nan
Mumbai
Milwaukee, WI
Arlington, VA
Washington, DC
nan
nan
SBA, BOS, JFK, EWR, SFO, LHR
Pasadena, CA
Los Angeles
nan
Washington, DC
nan
Toronto
salt lake city, utah
Boston, MA
nan
Boston, MA
nan
salt lake city, utah
Boston, MA
Boston, MA
nan
Boston, MA
nan
nan
Under your bed
Cincinnati, OH
salt lake city, utah
Long Island, NY
Boston, MA | San Jose, CA
nan
Washington, DC
nan
Belle MO
Russia, Россия
nan
Washington, DC
Massachusetts
nan
Washington, DC
nan
Belle MO
IL,USA
Washington, DC
nan
Washington, DC
nan
nan
Washingon, DC
IL,USA
Cincinnati
Islamabad
metro #wdc. prev #rva + #atl.
Washington, DC
nan
nan
Atlanta, GA
Washington DC
nan
Nashville, Tennessee
nan
nan
Washington, D.C.
Vancouver, WA
Washington, D.C.
nan
nan
Salisbury, MD
nan
Lexington
Lexington
nan
nan
Lexington
Lexington
Lexington
Lexington
Wilmington, NC
nan
Long Island, NY
Nashua, NH
nan
nan
nan
Neverland
nan
Bloomington, MN
Philadelphia, PA
Scottsdale, AZ
nan
nan
Philadelphia Suburbs
Washingon, DC
Vancouver, WA
Global
Global
Manc.
nan
South Shore, MA
Vancouver, WA
South Shore, MA
London, ON'
nan
London, ON'
Sydney, Australia
Charleston, SC
nan
Vancouver, WA
Wading River
South Shore, MA
Always Traveling
Vancouver, WA
Charlotte, North Carolina
Centerville, Ohio
Westbrook, CT
SportsCenter
DC / MIA
Vancouver, WA
nan
Scottsdale, AZ
NEXT D00R 2 U ( Chiangles )
Chicago
nan
nan
Raleigh, NC
Here, There and Everywhere
Philadelphia Suburbs
Here, There and Everywhere
Where Music meets Visual Media
St. Louis, MO
nan
Charleston
nan
Miss California United States
Miss California United States
ÜT: 38.907675,-76.944183
nan
Jupiter, FL
Nashville, TN
Vancouver, WA
nan
Russia, Россия
Charlotte, North Carolina
South Shore, MA
Portland, ME
Russia, Россия
Nashua, NH
South Shore, MA
nan
nan
Virginia Beach
nan
South Shore, MA
Russia, Россия
South Shore, MA
Wading River
nan
Boca Raton, FL
Charlottesville, Virginia
Russia, Россия
NJ
Boston, MA
Here, There and Everywhere
Research Triangle Park, N.C.
nan
nan
Miss California United States
Tampa
Vancouver, WA
nan
Boston
All Over the Damn Place
nan
Newtown, Pa
nan
nan
Charlotte, North Carolina
nan
Beavercreek Ohio
nan
Washington, DC, USA
Virginia Beach
Mission, Kansas
nan
nan
Chicago
nan
Portland, ME
New York
NC
nan
Boston, MA
Washington, DC
nan
nan
Here, There and Everywhere
nan
Philadelphia, PA
Portland, ME
Washington, DC
Washington, DC
NC
St. Louis, MO
nan
CT to Queens
Newtown, Pa
nan
nan
nan
nan
nan
nan
nan
nan
Beavercreek Ohio
Research Triangle Park, N.C.
nan
This is an AD account. 18+
Mission, Kansas
Philadelphia, PA
Detroit, MI
nan
Charlottesville, Virginia
Lima, OH
Boston, MA
Washington, DC
Queens
Nashville, TN
nan
New York
nan
Dallas, TX by way of Tampa, FL
Dallas, TX by way of Tampa, FL
nan
East Coast
nan
Beavercreek Ohio
Beavercreek Ohio
Ohio
Albany, NY
DC / MIA
nan
Fife, Dunfermline
CT to Queens
Lima, OH
NYC
Georgia
nan
Raleigh,NC
nan
Raleigh,NC
Worthington, Ohio
Ohio
nan
nan
Seattle, WA / 36,000 feet
nan
DC
Washington, DC
Ohio
nan
nan
Chicagoland Area
Austin, TX
nan
nan
Chicagoland Area
nan
nan
Queens
Queens
Seattle, WA
nan
nan
DC / MIA
Worthington, Ohio
New York
Dublin Raised | Brooklyn Based
Pittsburgh, Pa
Ohio
Chicagoland Area
Austin, TX
Ohio
Downers Grove
nan
Ohio
San Diego, CA
boston
MiamiHoustonDCHouston
MiamiHoustonDCHouston
MiamiHoustonDCHouston
MiamiHoustonDCHouston
nan
boston
boston
Maine
New York
New York
nan
ÜT: 35.029717,-80.9659
MiamiHoustonDCHouston
Anderson
nan
Atlanta, Georgia
nan
Dublin Raised | Brooklyn Based
New York
Charlotte, North Carolina
Downers Grove
Atlanta, GA
nan
nan
North West, UK
Charlotte, North Carolina
London, UK
Somewhere Creating
often underwater
Brighton
nan
nan
nan
nan
New York, NY
Mexico City
Liverpool
Frisco, Texas
MN
Albany, NY
Worcester, UK
MN
New York
Washington, DC
San Antonio, Republic of Texas
nan
Los Angeles, CA
nan
Tampa Bay
nan
nan
nan
Puerto Rico
Austin, but often Denver
New York Tri-State
Los Angeles, CA
Cincinnati, Ohio
Boston, MA
Mexico City
nan
DC to STL
United States
Chicago
SF, CA
Virginia
Newport Beach, CA
nan
oklahoma
Bellevue, WA
Las Vegas, NV
Dallas, TX
nan
North Saanich, BC
nan
nan
San Francisco
Brooklyn, NY and all over.
nan
nan
nan
Plano, TX
BK
the one and only TEXAS!!!!
Washington, DC
Mexico City
1/1 loner squad
MiniApple(s)
DFW, TX
NY (Globetrotter.ExBonaerense)
Denver, Colorado
Texas
nan
usa::fr::uk
Los Angeles, CA
nan
Bangkok, Thailand
North Saanich, BC
nan
Brooklyn, NY and all over.
nan
the one and only TEXAS!!!!
nan
Las Vegas, NV
United States
New York
NYC
Arkansas
new york city
new york city
new york city
DFW, TX
new york city
MiniApple(s)
Great State of Texas
City by the Bay
Greater New England Area
indialantic, fl
nan
nan
Connecticut
nan
CA
513/lexingtonKY
nan
Astoria, OR
nan
nan
nan
Little Rock, AR
nan
Posted
NY (Globetrotter.ExBonaerense)
nan
North Saanich, BC
nan
Washington, DC
nan
Denver, Colorado
nan
Wellesley, MA
Wellesley, MA
nan
USA
Dallas via NYC via the OC
Wellesley, MA
nan
Raleigh NC
East Coast CT.
Virginia
alexandria, va
Everywhere
NYC
United States
nan
BK
new england
nan
Arkansas
LBK
Arkansas
Las Vegas, NV
Washington, DC
nan
New York/Nicaragua/Miami Beach
nan
nan
Washington, DC
Southern Suburbia
Belle MO
Plano, Texas
Little Rock, AR
nan
Dallas via NYC via the OC
DFW, TX
Miami
Euless, Texas
Edmond, Oklahoma
nan
San Diego
nan
nan
nan
on @TheJR
Los Angeles
Caribbean, New York and Miami.
nan
nan
Sunnyside, NY
Texas
Thataway
nan
nan
nan
nan
nan
New York, NY
Mexico City
Liverpool
Frisco, Texas
MN
Albany, NY
Worcester, UK
MN
New York
Washington, DC
San Antonio, Republic of Texas
nan
Los Angeles, CA
nan
Tampa Bay
nan
nan
nan
Puerto Rico
Austin, but often Denver
New York Tri-State
Los Angeles, CA
Cincinnati, Ohio
Boston, MA
Mexico City
nan
DC to STL
United States
Chicago
SF, CA
Virginia
Newport Beach, CA
nan
oklahoma
Bellevue, WA
Las Vegas, NV
Dallas, TX
nan
North Saanich, BC
nan
nan
San Francisco
Brooklyn, NY and all over.
nan
nan
nan
Plano, TX
BK
the one and only TEXAS!!!!
Washington, DC
Mexico City
1/1 loner squad
MiniApple(s)
DFW, TX
NY (Globetrotter.ExBonaerense)
Denver, Colorado
Texas
nan
usa::fr::uk
Los Angeles, CA
nan
Bangkok, Thailand
North Saanich, BC
nan
Brooklyn, NY and all over.
nan
the one and only TEXAS!!!!
nan
Las Vegas, NV
United States
New York
NYC
Arkansas
new york city
new york city
new york city
DFW, TX
new york city
MiniApple(s)
Great State of Texas
City by the Bay
Greater New England Area
indialantic, fl
nan
nan
Connecticut
nan
CA
513/lexingtonKY
nan
Astoria, OR
nan
nan
nan
Little Rock, AR
nan
Posted
NY (Globetrotter.ExBonaerense)
nan
North Saanich, BC
nan
Washington, DC
nan
Denver, Colorado
nan
Wellesley, MA
Wellesley, MA
nan
USA
Dallas via NYC via the OC
Wellesley, MA
nan
Raleigh NC
East Coast CT.
Virginia
alexandria, va
Everywhere
NYC
United States
nan
BK
new england
nan
Arkansas
LBK
Arkansas
Las Vegas, NV
Washington, DC
nan
New York/Nicaragua/Miami Beach
nan
nan
Washington, DC
Southern Suburbia
Belle MO
Plano, Texas
Little Rock, AR
nan
Dallas via NYC via the OC
DFW, TX
Miami
nan
Itatiba/SP
Belle MO
Columbus, Ohio
nan
erie pa
Euless, Texas
DFW Area
Los Angeles
nan
nan
Euless, Texas
Denton County, Texas
nan
United States of America
DFW Airport, TX
nan
DFW Airport, TX
Texas
nan
nan
United States of America
nan
nan
nan
JAWJA
Boston
Belle MO
East Coast/New England
Chicago
BK
nan
Belle MO
Pittsburgh, PA
Dallas, Texas
nan
nan
Deep in the Heart of Texas
Castellon & Daytona Beach FL
San Diego, CA
nan
nan
Cary, NC
JAWJA
United States of America
nan
north carolina
nan
nan
nan
Mount Dora, Florida
Wellesley, MA
my top secret gaming facility
Texas
nan
nan
London, UK
usa::fr::uk
✅
Lake Arrowhead
Pirate Isle, Arcadia
New York, NY
nan
ÜT: 26.72488,-80.13666
nan
Los Angeles, CA
usa::fr::uk
Worldwide
san francisco califorania
usa::fr::uk
san francisco califorania
nan
nan
nan
nan
Largo florida
nan
Miami, New York, Boston
nan
Toronto, Ontario, Canada
Toronto, Ontario, Canada
Pirate Isle, Arcadia
nan
Toronto, Ontario, Canada
New York, NY
nan
nan
nan
nan
Wichita, KS
nan
nan
USA
nan
nan
nan
Washington, DC
nan
Wichita, KS
nan
nan
nan
nan
USA
Planet Earth
Chicago
nan
Washington, DC
Harrison, New York
nan
nan
Washington DC
Washington, DC
Denver, CO
Chicago, IL
Washington, DC
nan
nan
nan
nan
nan
USA
Washington DC
nan
nan
nan
nan
Boston, MA
World Wide.
USA
nan
Denver, CO
nan
nan
Boston, MA
Washington, D.C.
nan
nan
ÜT: 40.755844,-73.945229
nan
nan
iPhone: 37.621227,-122.386002
iPhone: 37.621227,-122.386002
Pittsburgh, PA
Fort Myers
nan
Fort Myers
Pittsburgh, PA
Pittsburgh, PA
iPhone: 37.621227,-122.386002
Greater New England Area
Greater New England Area
Los Angeles
Fort Myers
usa::fr::uk
Dallas,Tx
Fort Myers
nan
Nevada
Pittsburgh, PA
iPhone: 37.621227,-122.386002
Charlotte, NC
Fort Myers
Fort Myers
iPhone: 37.621227,-122.386002
nan
Dallas,Tx
nan
East Coast/New England
nan
Texas
iPhone: 37.621227,-122.386002
the one and only TEXAS!!!!
Surrey, UK
usa::fr::uk
Toronto, Ontario, Canada
usa::fr::uk
East Coast/New England
nan
the one and only TEXAS!!!!
Surrey, UK
nan
usa::fr::uk
Chicago
Chicago
usa::fr::uk
Surrey, UK
Indianapolis
nan
nan
nan
Arlington, VA
nan
Newark NJ
Middle earth
Washington, D.C.
Toronto, Ontario, Canada
Toronto, Ontario, Canada
Toronto, Ontario, Canada
Waco, TX
Toronto, Ontario, Canada
Glasgow via Cork
Texas
nan
nan
Missouri State University
Texas
nan
nan
SR2
nan
nan
St. Louis, MO
san francisco
nan
Miami
Glasgow via Cork
Phila, PA
nan
nan
Los Angeles
Somewhere In The World...
Greater Boston Area
northern ireland
nan
Winston-Salem, NC
Glasgow via Cork
Winston-Salem, NC
Miami
Miami
Winston-Salem, NC
Waynesville, OHIO
Metropolis
Metropolis
Waynesville, OHIO
Wichita, Kansas
Greater Boston Area
Wichita, Kansas
KFDK
Wichita, Kansas
Wichita, Kansas
nan
Waynesville, OHIO
KFDK
Belle MO
Miami
Waynesville, OHIO
nan
Boston
Dallas, TX
McKinney TX
nan
Dallas, TX
McKinney TX
McKinney TX
McKinney TX
nan
McKinney TX
Mitten
McKinney TX
McKinney TX
nan
nan
nan
nan
nan
nan
nan
nan
eating curly fries w/ the wife
nan
Manchester
Manchester
nan
nan
Manchester
Manchester
nan
Luxembourg
Nashville/Space
nan
Manchester
nan
eating curly fries w/ the wife
Crestwood
Nashville/Space
nan
nan
Northern Virginia
nan
nan
Nashville/Space
Finn between Detroit & Toledo
nan
Nashville/Space
Englewood, Florida
Nashville/Space
Syd.
Finn between Detroit & Toledo
Finn between Detroit & Toledo
New Delhi
nan
nan
Seattle
127.0.0.1
Cumbria
Beijing, China
Tucson
Manhattan
new orleans, la
Texas
nan
nan
nan
Edmond, Oklahoma
nan
nan
'Straya'
Wiltshire, UK
san francisco califorania
Nevada
Edmond, Oklahoma
Nevada
Nevada
North Hollywood
nan
North Hollywood
North Hollywood
North Hollywood
nan
Nevada
nan
san francisco califorania
The 2nd star to the right
Seattle, WA
The 2nd star to the right
nan
nan
san francisco califorania
nan
nan
las vegas/nashville, TN
las vegas/nashville, TN
Ohio
nan
las vegas/nashville, TN
Ohio
las vegas/nashville, TN
Moore, OK & Woodway, TX
las vegas/nashville, TN
Houston
Houston
nan
nan
nan
Houston
USA
Houston
Moore, OK & Woodway, TX
nan
Moore, OK & Woodway, TX
Houston
Houston
The Great State of TEXAS
nan
Moore, OK & Woodway, TX
nan
N to da Y to da C
N to da Y to da C
nan
nan
Living my dreams in motion
nan
nan
nan
nan
MIA || CHI || NYC
nan
nan
Founder of #MovieChurch
luke.asper@gmail.com
nan
Stamford, CT
Greenbow, Alabama
nan
nan
Stamford, CT
nan
nan
College Station
nan
Founder of #MovieChurch
district of columbia
nan
nan
LA
New York, NY
Chicago, IL
WARNING: Explicit language
nan
nan
nan
nan
iPhone: 40.829401,-73.926223
College Station
Left hand seat in flightdeck
nan
WARNING: Explicit language
nan
Stamford, CT
nan
Arlington VA
College Station
Arlington VA
nan
LA
The Great State of TEXAS
Cincity, ❤️hio
Left hand seat in flightdeck
The Great State of TEXAS
Orlando, FL
nan
Harrison, New York
Chicago, Il
Finn between Detroit & Toledo
nan
Arlington VA
nan
nan
nan
San Antonio, TX / Boston, MA
New York, NY
nan
nan
Greenbow, Alabama
Argentina
North Texas
Orlando, FL
World Wide
Los Angeles, CA
Sugarhood, SLC
nan
SE USA
nan
The Great State of TEXAS
nan
nan
nan
New York, NY
nan
Washington, D.C.
nan
LA
Belleville
Tampa, Florida
Orlando, FL
nan
Dallas,Tx
Texas
Los Angeles, CA
nan
North Texas
nan
nan
The gym, having a pizza.
nan
nan
Orange County, CA
nan
nan
Bedford, NH
Washington, D.C.
Mexico DF
nan
Los Angeles, CA
Bedford, NH
eating curly fries w/ the wife
Orlando, FL
Miami
AJ's Heart
nan
nan
Washington, DC via Wisconsin
Louisville KY
Los Angeles, CA
nan
kansas city
Chicago
Seattle, WA, USA
SE USA
Belleville
Belleville
Belleville
Belleville
New York
Belleville
Belleville
nan
nan
Dallas, TX
nan
Miami FL
nan
Minnesota
Kansas City, MO
nan
nan
nan
Waco, TX
Miami, FL
Los Angeles, CA
Belle MO
nan
Miami, FL
nan
Chicago
Belle MO
Houston
Chicago
Los Angeles
nan
nan
nan
eating curly fries w/ the wife
ÜT: 34.658262,-86.479396
nan
Seattle, WA, USA
Los Angeles, CA
nan
nan
USA
PDX JFK SFO BDL
Kansas City, MO
nan
Charlotte, NC
nan
Cleveland, OH
Minnesota
Minnesota
Los Angeles
San Diego
#Omaha
nan
nan
Brevard, NC
Cleveland, OH
nan
about.me/ajdelgado
The Onion Land
Chicago
Atlanta, GA USA
Atlanta, GA USA
Mexico
Miami
nan
Cleveland, OH
nan
'Straya'
San Antonio Texas
New York
New York
nan
Austin, Texas area
Miami
New York Tri-State
Belle MO
nan
México
SE USA
Harrison, New York
nan
Lake County, Illinois
Austin, TX
nan
nan
Sassy southerner in Chicago
Houston
nan
Kansas City, MO
Menomonee Falls, WI, USA
Sassy southerner in Chicago
Minnesota
nan
Somewhere In The World...
Cleveland, OH
Cleveland, OH
☀️SoFla☀️
South Wales
nan
nan
North Hollywood
Charlotte,NC
Cleveland, OH
NC by day, DC by night....
nan
New York
Seattle, WA
Seattle, WA
Austin, Texas area
San Diego, CA
new york city
Ft. Lauderdale
new york city
new york city
nan
new york city
Las Vegas, NV
nan
Lake County, Illinois
nan
nan
nan
nan
nan
Manhattan
Charlotte, NC
Cleveland, OH
nan
nan
South Wales
San Diego
Winterville NC
nan
Hants/Surrey, UK
Washington, D.C.
Iowa City, IA
NYC - Miami - Chicago
Dallas, or sometimes Detroit
california
☀️SoFla☀️
Ithaca, NY
PDX JFK SFO BDL
New York, NY
nan
nan
nan
nan
nan
nan
Kansas City, Missouri
Chicago, IL
nan
Frisco, Texas
Michigan, US
Washington, D.C.
Tampa, Florida
Oakland, CA
Tampa, Florida
Somewhere on Tyneside
McKinney TX
McKinney TX
new york city
new york city
Ft. Lauderdale
new york city
new york city
new york city
nan
'Straya'
nan
new york city
ÜT: -37.82304,144.961311
nan
North Hollywood
St. Louis, Missouri USA
nan
Chicago!
nan
I am from Internet!
Orlando, FL
Lake County, Illinois
nan
nan
Lake County, Illinois
Boston
nan
Lake County, Illinois
Lee's Summit
New York, New York
Springfield, MO
NYC - Miami - Chicago
Brooklyn to Across the Globe
Cambridge, MA, USA
OREGON
Dallas, TX, USA
nan
New York, NY
nan
Preferably on a plane
birmingham, usa
Los Angeles, CA
NYC
Manchester
McKinney TX
McKinney TX
I'm everywhere.
McKinney TX
nan
Manchester
Manchester
nan
Gold Coast, Australia
nan
Cambridge, MA, USA
Cambridge, MA, USA
Orlando, FL
San Francisco
San Francisco
Cuenca, Azuay, Ecuador
nan
South
Gilbert, AZ
Michigan, US
NYC
nan
DMV
The Specific Ocean
London
OREGON
Milwaukee
New York
PNW
Washington, D.C
NYC
nan
BTR/DCA/IAD/MSY - etc
Cambridge, MA, USA
birmingham, usa
Cambridge, MA, USA
Cambridge, MA, USA
Dublin, Ireland
BTR/DCA/IAD/MSY - etc
Dublin, Ireland
The Republic of Texas
Bartlesville, OK
Lake County, Illinois
Present
☀️SoFla☀️
Washington DC
Wisconsin
Los Angeles, CA
NYC
nan
nan
Las Vegas
nan
nan
nan
Las Vegas, NV
Boston, MA - Scottsdale, AZ
nan
nan
nan
nan
Greenbow, Alabama
nan
Gilbert, AZ
nan
CHI/ATX
nan
Gilbert, AZ
nan
Gilbert, AZ
Gilbert, AZ
Washington, D.C.
The Republic of Texas
Michigan, US
nan
Northern Virginia
Charlottesville, VA
Fort Lauderdale-Dallas-Boston
Los Angeles, CA
nan
Bartlesville, OK
Brooklyn, NY
Milky Way:Orion Arm:Earth:DFW
san marcos,ca
Boston, MA
DFW
nan
nan
nan
DFW
Turks and Caicos Islands
nan
ÜT: 41.498967,2.186957
san marcos,ca
The Specific Ocean
The Specific Ocean
PNW
NYC
Brooklyn, NY
New York, NY
Springfield, MO
Iowa City, IA
nan
Los Angeles, CA
Here. But I want to be there.
Iowa City, IA
Iowa City, IA
nan
Earth
nan
nan
nan
New York, NY
San Diego
Seattle, WA
nan
Hutchinson, KS
Chapel Hill, NC
DC
nan
Los Angeles, CA
Vancouver
nan
Guadalajara, México
nan
nan
DC/OKC
Michigan, US
Las Vegas
Boston, MA
Los Angeles, CA
Northern Virginia
Dallas, Tx, USA
Charlottesville, VA
Austin, TX.
New York, NY
nan
nan
nan
nan
nan
nan
nan
Turks and Caicos Islands
nan
New York, NY
nan
nan
nan
nan
Dallas
Greater Geelong
Dallas
Dallas
nan
San Diego
nan
nan
MPLS/ATX/BK
Milwaukee
Michigan, US
Hampton Roads
Seattle, WA
nan
Wisconsin
my top secret gaming facility
nan
North Carolina
London
iPhone: 41.890884,-87.631195
nan
Bennington, NE
Scotland
Netherlands
Netherlands
New York, NY
nan
Houston
Jacksonville Florida
nan
Bennington, NE
KC
ÜT: 41.498967,2.186957
Dallas, Tx, USA
nan
N 44°0' 0'' / W 92°25' 0''
San Diego
nan
nan
21.791267,-72.189674
nan
Denver
nan
21.791267,-72.189674
nan
nan
Memphis, TN
New York
Chicago!
nan
nan
nan
New York
DC
nan
nan
Seattle, WA
PR/Miami/San Francisco
nan
nan
nan
Cary, NC
Brooklyn
nan
Chicago, IL
BPM, WMC, DEMF, BM, ADE, etc
Seattle, WA
Chicago
Allen, TX
Allen, TX
nan
nan
Boston, MA
nan
Seattle, WA
my top secret gaming facility
San Diego
New York, NY
nan
Chile
Scotland
San Diego
Belle MO
nan
Bennington, NE
nan
Phoenix, AZ
NYC
nan
Chicago, IL
Texas
Chicago, IL
Charlottesville, VA
New York
✡ Los Angeles ✡
ohio,panama
Dallas, Texas
Chicago Subs
Helsinki Airport
Michigan, US
nan
Milky Way:Orion Arm:Earth:DFW
Dallas
NY
Dallas
Brevard, NC
New York | Hong Kong
Toronto
Long Island / Albany
Brooklyn, NY
ÜT: 37.77581,-122.419796
Innisfail, Alberta
Toronto
nan
Miami Beach
nan
Allen, TX
San Diego, CA
the one, the only.... NYC
nan
nan
nan
Bromyard
'Merica
nan
NYC area
milford.
Memphis, TN
Boston
nan
Los Angeles
Los Angeles, California
ÜT: 41.5333,-93.62944
nan
Chicago,IL
San Diego, CA
Cary, NC
nan
Lantana, Tx USA
nan
Milwaukee, WI
Dallas, TX
nan
iPhone: 38.961334,-77.006119
nan
San Francisco, CA
Aspen, Colorado
Amityville, NY
nan
nan
suburban Chicago, IL
São Paulo
nan
New York
nan
New York, NY
KFAR
Toronto
nan
Brooklyn, NY
nan
lydham shropshire uk
Belle MO
NYC
lydham shropshire uk
nan
nan
Allen, TX
lydham shropshire uk
nan
nan
nan
nan
nan
Milky Way:Orion Arm:Earth:DFW
Could be Anywhere
nan
nan
nan
nan
nan
'Merica
nan
QCA
nan
Buenos Aires, Argentina
Where the wind takes me
Chicago, IL
nan
Charlottesville, VA
nan
nan
nan
New York, NY
Monterrey, Nuevo Leon
nan
Seattle
nan
ÜT: 37.77581,-122.419796
VB/VA
Boston, MA
ÜT: 37.77581,-122.419796
VB/VA
ÜT: 37.77581,-122.419796
Santa Monica, CA
Chicago,IL
nan
Everywhere
Chicago, IL
VB/VA
nan
QCA
Guadalajara, México
nan
iPhone: 38.961334,-77.006119
nan
BK
nan
Round Rock, TX
Dallas, TX
New York
BK
nan
nan
nan
Seattle
New York
⋆city of lost angels⋆
New York
Los Angeles
nan
nan
Saint LOUis
New York, NY
Brooklyn, NY
nan
nan
Portsmouth UK
lydham shropshire uk
nan
New York City
Boston
milford.
New York
Los Angeles
Branchburg, NJ
nan
nan
iPhone: 38.961334,-77.006119
nan
nan
New England
Boston, MA
nan
Chesapeake, VA
nan
nan
nan
nan
Belle MO
nan
Great Barrington, MA
nan
nan
College Station, Tx
nan
nan
nan
Belle MO
nan
nan
Boston, MA
Inter/Outer-Continental U.S.
Memphis, TN
Boston, MA
Memphis, TN
New England
Chicago,IL
Boston, MA
Boston, MA
Renton, WA
Milwaukee, WI
Chicago,IL
Evansville - LA - NOLA
nan
nan
nan
nan
nan
New York
nan
nan
nan
Portland, Oregon
Dallas, TX
Florida
nan
Brantford
nan
nan
Everywhere
Miami
Austin, TX
Dallas, TX
nan
nan
nan
nan
Richmond, VA
nan
Everywhere
nan
nan
Liverpool
nan
ÜT: 42.63352,-83.624194
New York
St. Catharines
prensa: @talentolatinotv
San Francisco
St. Catharines
⋆city of lost angels⋆
fort worth
nan
Texas
nan
BK
New England
New England
nan
Texas
Brklyn by way of San Franciso
BK
nan
BK
Chicago,IL
BK
Evansville - LA - NOLA
Renton, WA
BK
New England
Plano, TX
suburban Chicago, IL
New England
New York
New England
nan
Chicago, IL
Portland, Oregon
Brevard, NC
Belle MO
Los Angeles
Portland, Oregon
Plano, TX
San Francisco, CA
nan
nan
usa
Nashville
Russell, KS
Los Angeles
The Republic of Texas
nan
nan
nan
@MontgomeryCoMD / SYR / HHI
nan
nan
Texas Tech University USA
Boston, MA
nan
DFW
nan
Tampa, Florida
Chicago, IL
Minneapolis, MN
nan
nan
nan
Las Vegas
nan
Los Angeles
Bentonville, Arkansas
SR2
nan
Bentonville, Arkansas
Atlanta, GA
Atlanta, GA
Los Angeles
Youtube | crackedchinacup
Boston, MA
nan
Texas
nan
nan
nan
nan
Bentonville Arkansas
Los Angeles
Boston, MA
Boston
nan
London
Manhattan, NY
Memphis
DFW
nan
Toronto
Los Angeles
Columbia, MO
nan
Indianapolis, IN
Chicago
Fort Smith, Ark.
nan
Brooklyn by way of Chicago
Baltimore, Maryland
Planet Earth
nan
Northwest Arkansas
cali bound
@MontgomeryCoMD / SYR / HHI
Southern Wisconsin
Las Vegas
nan
nan
Seattle
Texas
nan
Ando por el mundo
Ando por el mundo
Ando por el mundo
Dallas, TX
Bellevue, WA
nan
Fort Worth, TX
Bentonville, Arkansas
SR2
Brooklyn by way of Chicago
Hawaii/Dallas/Tucson/LA
nan
Toronto
nan
nan
nan
Texas
Innisfail, Alberta
nan
New York
Southern California
nan
nan
Albuquerque,New Mexico
Bentonville Arkansas
New York, NY
nan
London
Memphis
Memphis, TN
nan
nan
nan
New York, NY
Texas
nan
Memphis, TN
nan
Saint Maur des Fossés
nan
Saint Maur des Fossés
Bentonville Arkansas
Brooklyn by way of Chicago
New York, NY
nan
nan
Hertfordshire & London
Indianapolis
nan
Baltimore, Maryland
Fort Smith, Ark.
nan
nan
nan
nan
St. Louis, Mo
ÜT: 42.63352,-83.624194
Wichita Falls, Texas
Miami Beach
nan
Tokyo, Japan
Napa, CA
Saint Maur des Fossés
Bellevue, WA
Southern California
San Diego
Chicago, IL
cali bound
Dallas, TX
nan
London, England
New York, NY
Texas
LA via NY via SP
nan
Brooklyn, NY
nan
nan
nan
nan
Brooklyn, NY
nan
Indianapolis
Brooklyn, NY
nan
nan
Indianapolis
Brooklyn, NY
Brooklyn, NY
nan
Chicago, IL
Miami Beach
Southern California
A tiny place in the universe
New York, NY
nan
New York City
Glastonbury, CT
nan
Chicago, IL
London
nan
Plano, Texas
nan
nan
nan
North Carolina
nan
nan
chapel hill, nc
chicago, il
Amityville, NY
Charleston, SC
Plano, Texas
London, UK
nan
Plano, Texas
nan
nan
nan
London
nan
Saint Maur des Fossés
Los Angeles, CA
nan
nan
Hertfordshire & London
Michigan
Everywhere
nan
Bexleyheath
nan
my top secret gaming facility
London
UK
nan
New York | Hong Kong
Buenos Aires
cali bound
nan
Alexandria, VA
Vienna, Austria
Rockville, MD
nan
San Diego
Edmonton
San Diego
Edmonton
Edmonton
San Diego
Edmonton
Edmonton
North Carolina
nan
nan
texas
San Diego
Illinos
San Diego
San Diego
Glasgow / Scotland
New York, NY
San Diego
San Diego
San Diego
San Diego
San Diego
San Antonio, Texas
http://about.me/miffsc
nan
nan
nan
Lafayette, LA
nan
Bay Area, CA
nan
nan
New York City
nan
nan
nan
nan
San Francisco
Left hand seat in flightdeck
nan
world
nan
nan
Renton, WA
Greater Geelong
nan
nan
Kansas City, MO USA
nan
nan
Londres
Londres
nan
Jerz
Arkansas
nan
nan
ÜT: 47.683801,-122.327443
nan
Pride Rock
Bay Area
nan
Scottsdale, AZ
nan
PDX JFK SFO BDL
New York City
nan
Plano, Texas
Dallas, Texas
nan
nan
Boston, MA
Plano, Texas
New York, NY
San Francisco, CA
Los Angeles
nan
nan
nan
nan
Brooklyn
nan
nan
Seattle, WA
El Paso, TX
Indianapolis
College Station
41.301208,-81.750618
Austin, TX
41.301208,-81.750618
Austin, TX
El Paso, TX
nan
Michigan
nan
Indianapolis, IN
Austin, TX
Austin, TX
Austin, TX
Natchitoches, LA
SEA
nan
Raleigh, NC
nan
dallas, TX
nan
New York, New York
Austin, TX
Charlotte, NC | Köln, NRW
Miami Beach
Memphis, TN
Austin, TX
SR2
Austin, TX
Vancouver - Ann Arbor
Miami Beach
nan
nan
Miami Beach
Glendale, Arizona
nan
nan
nan
nan
nan
nan
San Diego, CA
ÜT: 40.881241,-73.107717
nan
piffaca
nan
Boston, MA USA
nan
nan
Washington D.C.
nan
nan
Sloop
nan
Dallas, Texas
Birmingham, AL
nan
nan
San Francisco, CA
Midlothian, TX
Merion, PA
Finn between Detroit & Toledo
nan
Madison, WI
Raleigh, NC
nan
nan
Dallas
Cambridge, MA
Lost boys, Neverland
nan
Miami Beach, Florida
Miami Beach, Florida
San Francisco
ATX
nan
nan
Chicago
Cathedral Heights
Guatemala.
ig; tannersayre
Bloomington, IL
Kingsville, TX
nan
Los Angeles, CA
nan
Raleigh, NC
Portland, OR
nan
Raleigh, NC
Bloomington, IL
nan
Dallas | Los Angeles
Washington D.C.
Brooklyn, NY
Philadelphia, PA
nan
Philadelphia, PA
st paul, minnesota
Madison, WI
Philadelphia, PA
nan
nan
Lewes, DE, USA
Las Vegas
Philadelphia, PA
Dallas | Los Angeles
Houston TX
Philadelphia, PA
nan
Buda Texas
nan
nan
nan
Boston
Two Guns, Arizona
nan
Chicago, IL
Buda Texas
nan
Arlington, VA
North Hollywood
Arlington, VA
Toronto,Montreal,L.A,NY
Sarasota, FL
nan
Kansas City, MO
Sac City, IA
Leon. De los aldamas
New Haven, CT
Chicago
nan
St. Louis, Mo
nan
Wherevers not gonna get me hit
Dallas, Texas
Washington, D.C.
nan
Dallas, Texas
South Florida
nan
PDX JFK SFO BDL
MA
Montgomery Al
nan
Plano, Texas
Phoenix AZ
Instagram: @rad_mika
nan
nan
Dallas, Texas
nan
Chicago, IL
nan
tejas
West Palm Beach
Salt Lake City, USA
nan
nan
nan
Jacksonville Beach, Florida
Melbourne, Australia
Plano, Texas
New York City
New York City
West Palm Beach
Tucson
Portland, Orygun
Indianapolis
Windy City
nan
Portland, Orygun
Albuquerque,New Mexico
Omaha
nan
DC
nan
nan
Boston, MA
nan
nan
New York City
nan
nan
New York, NY
Los Angeles, CA
nan
Chicago, IL
Melbourne, Australia
Bloomington, IL
Carlsbad, So Cal from Hawaii
Denver, CO
nan
New York | Hong Kong
nan
New York City
Windermere (seattle)
Austin, Texas
Tampa, Fl
nan
Washington, DC
Chicago, Mumbai
nan
nan
Ithaca til May 2015
New York, NY
New York, NY
nan
New York, NY
midlothian, tx
SNY in NYC.
Boston
nan
nan
Kansas City, MO
Mind Tripping
New York, NY
Washington, DC
nan
nan
Washington, DC
nan
✨
NYCATL
Washington, DC
California
nan
New York, NY
Portland, Orygun
nan
NYCATL
nan
nan
nan
Santo Domingo
nan
nan
NYCATL
Santo Domingo
nan
Kitchen, office or on the road
Jackson County, Oregon
Fort Myers, Florida
New York, NY
Santo Domingo
Portland, Orygun
Washington, DC
Behind you, look again.
USA
The Ted Williams Tour
Washington, DC
Frisco, TX
nan
Kansas City, MO USA
USA
nan
Washington, DC
nan
Kansas City, MO USA
New York, NY
nan
nan
nan
nan
nan
London, UK
nan
nan
NYC/TX
nan
Left hand seat in flightdeck
nan
nan
Abbotsford
nan
Dallas Area, Texas
New York, NY
nan
L-Town
St. Catharines
St. Catharines
St. Catharines
nan
waco, texas
San Diego, CA
Tem conexao? To aqui entao!
Cambridge MA
Urbana, IL
Seattle, WA / 36,000 feet
Urbana, IL
Global
nan
Texas
NC by day, DC by night....
Buda Texas
DFW
Texas
Alexandria, VA
nan
DFW
Plano, Texas
Chicago, IL
Charlotte, NC | Köln, NRW
nan
NY
Charlotte, NC | Köln, NRW
Frisco, Texas
New York City
nan
New Orleans, La
nan
Innisfail, Alberta
nan
Austin, TX
nan
Cape Cod
San Antonio, TX
Toronto
Cape Cod
Milwaukee
nan
nan
Rossford, Ohio
nan
Guatemala.
ATX
nan
NYCATL
nan
Orlando, FL
Alexandria, VA
NYCATL
washington, dc
Philly
nan
nan
Arlington, VA
nan
60093
nan
London, England
Milwaukee
London, England
Massachusetts
Bay Area, CA
Englewood, Florida
Springfield, Illinois
Massachusetts
Redwood City, CA
nan
nan
NYCATL
nan
Milwaukee
nan
NYCATL
US
Seattle, WA
Dallas
Raleigh, NC
Albuquerque, New Mexico
Hutchinson, KS
Arlington, VA
Madison, WI
London, England
Albuquerque, New Mexico
Arlington, VA
Bay Area
Guatemala.
Weston, CT USA
Pekin
'Greatness has no limits'
Arkansas
nan
Kingsville, TX
San Diego, CA
nan
nan
Birmingham, AL
nan
Los Angeles
nan
nan
Los Angeles
nan
North of 146th
London
Far From Home
nan
Ciudad Juárez MX. [CJS/MMCS]
nan
O.K.C.
nan
nan
Pekin
nan
nan
Pekin
New York City
Pekin
nan
nan
UK
Brasil
Seat 4B
The Greatest City in The World
nan
nan
Milwaukee, WI
nan
Pekin
New York City
Innisfail, Alberta
Pekin
NYCATL
Springfield, MO
Pekin
Chicago, IL
New York City
Dallas, Texas
Newark, DE
nan
New York City
New York, NY
nan
Dallas, Texas
nan
Pekin
southeast
Dallas, Texas
Far From Home
Pekin
Philadelphia
nan
Philadelphia
nan
nan
Springfield, Illinois
nan
Buda Texas
Natchitoches, LA
KY
Jacksonville Beach, Florida
nan
KY
Indiana
ÜT: 38.830712,-77.110175
nan
New York
NYC
nan
Jacksonville Florida
nan
nan
nan
nan
I'm everywhere.
Lafayette, LA
Brooklyn
Indiana
Russell, KS
UK
nan
NY
San Francisco, California
Lewes, DE, USA
New York, NY
nan
nan
NYCATL
Chicago, IL
NYCATL
New York City
Springfield, Illinois
NYCATL
Lake Norman NC
KY
New York City
Corona Del Mar
nan
Los Angeles, CA
Miami
NYCATL
Los Angeles, CA
KY
Kitchen, office or on the road
The Hague
Orange County, CA
Tucson
KY
Boston, MA
Denver, CO
nan
Chicago, IL
nan
nan
El Paso, TX
Pocos de Caldas
nan
Kitchen, office or on the road
nan
Canterbury, Kent
Springfield, Illinois
Mexicali, B.C.
DC
Tampa, Fl
nan
Atlanta Ga && Traveling
SNY in NYC.
New York | Hong Kong
West Covina, CA
Rive-sud de Montréal
Southern Wisconsin
Ann Arbor, MI
nan
Pocos de Caldas
nan
Washington, DC
nan
São Paulo
São Paulo
nan
Kingston ontario Canada
Behind you, look again.
nan
Fort Myers, Florida
everywhere
nan
Portland, OR
nan
nan
New York, NY
washington, dc
ÜT: 40.881241,-73.107717
New York, NY
nan
Springfield, Illinois
ÜT: 40.881241,-73.107717
Cambridge, MA
Phoenix, AZ
Brookfield, WI
nan
saint paul
Vancouver, BC
nan
nan
Brookfield, WI
nan
nan
Santa Monica, CA
U.S.A.
Springfield, Illinois
NYC
nan
nan
NYC
Global
nan
hertz's couch
-тнαт νσι¢є ιиѕι∂є уσυя нєα∂
Charlotte, NC | Köln, NRW
Worldwide, based in Dallas, TX
Global
Somewhere East
nan
nan
Toronto
nan
Jacksonville Beach, Florida
New York City
Naperville, IL
Jerz
nan
Orlando, FL
New York, NY
Chicago
Orlando, FL
Chicago
College Station, TX
Chicago
Plano, Texas
nan
nan
College Station, TX
Philadelphia, PA
Cathedral Heights
Chicago, IL
Seattle, WA
nan
Alexandria, VA
SCL
Ocean Ridge, FL
Atl
nan
Tucson, Arizona
New York City
nan
Boston, MA
★ Chicago'ish ★
nan
☀️SoFla☀️
nan
in your fav mags & blogs!!!
nan
Boston, MA
Lima, OH
Arlington, VA
Reading MA
London
nan
O.K.C.
nan
USA
Washington DC
nan
Philadelphia, PA
DFW
nan
Portland, Orygun
nan
nan
Jeddah-Karachi X NYC
nan
nan
nan
Nashville TN
Nicosia, Cyprus
Austin, TX
dallas, tx
nan
San Francisco
Dallas, TX
nan
nan
nan
Manhattan, Kansas
Russell, KS
nan
Los Angeles, CA
Belleville
Washington DC
nan
San Francisco
Chicago
Shreveport, Louisiana
nan
Belleville
Cambridge, MA
Chicago
Dallas, Texas
Central Ohio
nan
Los Angeles, CA
nan
nan
Cool suburb N. of Dallas
Dallas, Texas
nan
nan
Cambridge, MA
nan
nan
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Miami
San Marcos, Texas
Los Angeles, CA
California
Rossford, Ohio
Great Abington, Cambridge, GB
UK
New York, NY
Great Abington, Cambridge, GB
Rossford, Ohio
Phoenix, AZ
nan
nan
Pocos de Caldas
Hawaii/Dallas/Tucson/LA
nan
nan
nan
nan
Tampa, Fl
New York, NY
nan
Flying Giraffe Travel
nan
san marcos,ca
Boston area
Teh internets
Rive-sud de Montréal
The Republic of Texas
Cambridge, Massachusetts
Corpus Christi
Champaign/Urbana, IL
Rossford, Ohio
Tucson
nan
nan
Dallas, TX
Pekin
Massachusetts
nan
on @TheJR
San Francisco, CA
nan
Dublin, Ireland
Washington, DC
waco, texas
nan
Dallas, TX
nan
nan
Plano, Texas
Tiffin, Ohio
The City of Big Shoulders
nan
nan
The City of Big Shoulders
Rossford, Ohio
nan
nan
Hawaii/Dallas/Tucson/LA
nan
North of 146th
nan
Trying to go everywhere!
Corona Del Mar
nan
ÜT: 29.717516,-95.505488
Tokyo, Japan
Los Angeles, CA
nan
São Paulo
970 Colorado
nan
nan
Chicago
nan
Chicago
Plano, Texas
Omaha
Chicago
nan
nan
Chicago
nan
nan
nan
Naperville, IL
Seattle, WA
Plano, Texas
nan
Orange County, CA
hertz's couch
nan
nan
New York City
Memphis, TN
San Jose, CA
Durham, NC
Atl
Tucson, Arizona
Pocos de Caldas
New York City
nan
Madison, WI
Los Angeles
Jeddah-Karachi X NYC
Alexandria, VA
Albuquerque,New Mexico
Plano, TX
In the kitchen
nan
Pocos de Caldas
nan
O.K.C.
Los Angeles, CA
Pocos de Caldas
nan
nan
Oxford, MS
Pekin
nan
Dallas, TX
iPhone: 40.829401,-73.926223
Tennessee
New Jersey
Los Angeles, CA
New Jersey
nan
nan
UK / Canada / etc
PA, NY & HI
Dallas, TX
College Station, TX
Ohio
nan
Ocean Ridge, FL
nan
Dallas, TX
NEW YORK CITY
nan
Capitol Hill D.C.
New Jersey
nan
Seattle, WA
nan
New Jersey
nan
PA, NY & HI
Pekin
nan
nan
Pekin
PA, NY & HI
L-Town
Dallas, Texas
nan
PA, NY & HI
Pekin
PA, NY & HI
Webster Groves, MO
Boston, MA
The City of Big Shoulders
Boston, MA
London
ÜT: 40.881241,-73.107717
nan
ÜT: 40.881241,-73.107717
Brooklyn
ÜT: 40.881241,-73.107717
The City of Big Shoulders
nan
Washington DC
New York, NY
nan
Santa Maria, Califoria
Pekin
nan
everywhere, all the time.
Brooklyn
Pekin
Belleville
Dallas, Texas
Ocean Ridge, FL
nan
Pekin
nan
Pekin
Brantford
San Francisco
Tyler, Texas
Brookings, SD
Los Angeles
New England
Chicago
Maidstone, UK
Los Angeles, CA
nan
Pekin
College Station, TX
Pekin
nan
Pekin
Pekin
Long Island, New York
Caribbean, New York and Miami.
Pekin
Pekin
NEONgarden
Pilot Point, Republic of Texas
Lake Arrowhead
Pekin
nan
Seattle, WA
nan
nan
Natchitoches, LA
nan
nan
Buenos Aires, Argentina
nan
Everett
Dallas, TX
Dallas, TX
nan
nan
Santa Monica, CA
nan
nan
Tokyo, Japan
Cool suburb N. of Dallas
Chelmsford, MA
Old City Philly
Dublin, Ireland
nan
Washington, DC
North Saanich, BC
nan
Dallas, TX
Fort Worth, TX
nan
National, based in Texas
nan
nan
Pekin
nan
Pekin
KY
Pekin
Los Angeles, CA
London
KY
Nashville TN
Pueblo, CO
Naperville, IL
nan
KY
KY
London
London
970 Colorado
Central Ohio
Chicago
nan
Washington DC
New York City
Columbus, OH, USA
nan
nan
Milwaukee County, Wisconsin
New York, New York
nan
nan
nan
East Coast
Buenos Aires, Argentina
nan
Washington, DC
Waco, TX
Chapel Hill, NC
US
nan
Los Angeles
nan
nan
Texas
Nigeria,lagos
New Jersey
dallas, TX
In [22]:
tweets['NY'] = 0
In [23]:
def NY(row):
if (row['tweet_location'] != row['tweet_location']):
return 0
if (row['tweet_location'].find("NY") != -1) or (row['tweet_location'].find("New York") != -1):
return 1
else:
return 0
tweets['NY'] = tweets.apply(NY, axis=1)
In [24]:
tweets_ny = tweets[tweets['NY'] == 1].copy()
In [25]:
df = tweets_ny.groupby('negativereason').size().reset_index(name='counts')
n = df['negativereason'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['negativereason'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})
plt.gca().set_xticklabels(df['negativereason'], rotation=60, horizontalalignment= 'right')
plt.title("Tweets with different reasons for dissatisfaction", fontsize=22)
plt.ylabel('# Tweets')
plt.show()
В целом датафрейм не самый информативный. Компании в нём только американские, а период времени - вторая половина февраля 2015. Он подходит скорее как пример работы с такими данными и для того, чтобы показать потенциал такого исследования для авиакомпаний. Проведём более подробное исследование на следующих наборах данных.
In [ ]:
2) Skytrax¶
Данные отсюда: https://www.kaggle.com/efehandanisman/skytrax-airline-reviews
Посмотрим на наши данные по отзывам.
Основные столбцы в нём:
- airline (название авиакомпании)
- overall (общая оценка)
- review_date (дата публикации)
- customer_review (текст отзыва)
- traveller_type (тип поездки)
- cabin (класс)
- route (откуда и куда был полёт)
- seat_comfort, cabin_service, food_bev, entertainment, ground_service, value_for_money (оценки полёта по 6 параметрам по пятибальной шкале)
- recommended (рекомендует ли автор авиакомпанию)
In [26]:
skytrax = pd.read_excel('capstone_airline_reviews3.xlsx')
skytrax.head()
/opt/anaconda3/lib/python3.7/site-packages/xlrd/xlsx.py:266: PendingDeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
Out[26]:
| airline | overall | author | review_date | customer_review | aircraft | traveller_type | cabin | route | date_flown | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | Turkish Airlines | 7.0 | Christopher Hackley | 8th May 2019 | ✅ Trip Verified | London to Izmir via Istanb... | NaN | Business | Economy Class | London to Izmir via Istanbul | 2019-05-01 00:00:00 | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | yes |
| 2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | Turkish Airlines | 2.0 | Adriana Pisoi | 7th May 2019 | ✅ Trip Verified | Istanbul to Bucharest. We ... | NaN | Family Leisure | Economy Class | Istanbul to Bucharest | 2019-05-01 00:00:00 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
| 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Удалим совсем пустые строки, они тут зачем-то через одну в изначальных данных
In [27]:
skytrax.dropna(subset=['review_date'], inplace=True)
skytrax.head()
Out[27]:
| airline | overall | author | review_date | customer_review | aircraft | traveller_type | cabin | route | date_flown | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Turkish Airlines | 7.0 | Christopher Hackley | 8th May 2019 | ✅ Trip Verified | London to Izmir via Istanb... | NaN | Business | Economy Class | London to Izmir via Istanbul | 2019-05-01 00:00:00 | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | yes |
| 3 | Turkish Airlines | 2.0 | Adriana Pisoi | 7th May 2019 | ✅ Trip Verified | Istanbul to Bucharest. We ... | NaN | Family Leisure | Economy Class | Istanbul to Bucharest | 2019-05-01 00:00:00 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
| 5 | Turkish Airlines | 3.0 | M Galerko | 7th May 2019 | ✅ Trip Verified | Rome to Prishtina via Ista... | NaN | Business | Economy Class | Rome to Prishtina via Istanbul | 2019-05-01 00:00:00 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | no |
| 7 | Turkish Airlines | 10.0 | Zeshan Shah | 6th May 2019 | ✅ Trip Verified | Flew on Turkish Airlines I... | A330 | Solo Leisure | Economy Class | Washington Dulles to Karachi | April 2019 | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | yes |
| 9 | Turkish Airlines | 1.0 | Pooja Jain | 6th May 2019 | ✅ Trip Verified | Mumbai to Dublin via Istan... | NaN | Solo Leisure | Economy Class | Mumbai to Dublin via Istanbul | 2019-05-01 00:00:00 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
Первое, что меня заинтересовало - это авиакомпании. Их тут много, попробуем их как-то обработать. Для начала посчитаем количество упоминаний каждой из них и процент от общего количества
In [28]:
skytrax_airlines = skytrax.groupby('airline').size().reset_index(name='counts')
print(len(skytrax_airlines))
skytrax_airlines.head()
81
Out[28]:
| airline | counts | |
|---|---|---|
| 0 | ANA All Nippon Airways | 473 |
| 1 | Adria Airways | 85 |
| 2 | Aegean Airlines | 531 |
| 3 | Aer Lingus | 715 |
| 4 | Aeroflot Russian Airlines | 503 |
In [29]:
def airline(row):
return row['counts'] / len(skytrax) * 100
skytrax_airlines['%'] = skytrax_airlines.apply(airline, axis=1)
skytrax_airlines.sort_values('counts', inplace=True, ascending=False)
skytrax_airlines = skytrax_airlines.reset_index()
skytrax_airlines.head()
Out[29]:
| index | airline | counts | % | |
|---|---|---|---|---|
| 0 | 63 | Spirit Airlines | 2934 | 4.449027 |
| 1 | 17 | American Airlines | 2867 | 4.347431 |
| 2 | 73 | United Airlines | 2829 | 4.289808 |
| 3 | 22 | British Airways | 2811 | 4.262514 |
| 4 | 24 | Cathay Pacific Airways | 2402 | 3.642319 |
In [30]:
del skytrax_airlines['index']
skytrax_airlines.head()
Out[30]:
| airline | counts | % | |
|---|---|---|---|
| 0 | Spirit Airlines | 2934 | 4.449027 |
| 1 | American Airlines | 2867 | 4.347431 |
| 2 | United Airlines | 2829 | 4.289808 |
| 3 | British Airways | 2811 | 4.262514 |
| 4 | Cathay Pacific Airways | 2402 | 3.642319 |
Выделим 15 самых популярных, а остальные объединим вместе и отобразим это на круговой диаграмме
In [31]:
line = list(skytrax_airlines[15:].sum())
skytrax_airlines = skytrax_airlines[:15].copy()
skytrax_airlines.loc[15] = ["Other", line[1], line[2]]
skytrax_airlines
Out[31]:
| airline | counts | % | |
|---|---|---|---|
| 0 | Spirit Airlines | 2934 | 4.449027 |
| 1 | American Airlines | 2867 | 4.347431 |
| 2 | United Airlines | 2829 | 4.289808 |
| 3 | British Airways | 2811 | 4.262514 |
| 4 | Cathay Pacific Airways | 2402 | 3.642319 |
| 5 | Air Canada rouge | 2192 | 3.323881 |
| 6 | Emirates | 1786 | 2.708235 |
| 7 | China Southern Airlines | 1722 | 2.611188 |
| 8 | Frontier Airlines | 1624 | 2.462584 |
| 9 | Ryanair | 1566 | 2.374634 |
| 10 | Delta Air Lines | 1547 | 2.345823 |
| 11 | Turkish Airlines | 1491 | 2.260906 |
| 12 | Qatar Airways | 1445 | 2.191154 |
| 13 | Lufthansa | 1421 | 2.154761 |
| 14 | Air India | 1382 | 2.095622 |
| 15 | Other | 35928 | 54.480113 |
In [32]:
fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)
data = skytrax_airlines['counts']
categories = skytrax_airlines['airline']
explode = [0] * len(skytrax_airlines)
def func(pct, allvals):
absolute = int(pct/100.*np.sum(allvals))
return "{:.1f}%".format(pct, absolute)
wedges, texts, autotexts = ax.pie(data,
autopct=lambda pct: func(pct, data),
textprops=dict(color="w"),
colors=plt.cm.Dark2.colors,
startangle=140,
explode=explode)
ax.legend(wedges, categories, title="Airlines", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
plt.setp(autotexts, size=10, weight=700)
ax.set_title("Airlines: Pie Chart")
plt.show()
Дальше я прорисовал гистограммы по всем значимым столбцам, по ним можно отследить основные тренды в данных и распределение этих параметров
In [33]:
df = skytrax.groupby('traveller_type').size().reset_index(name='counts')
n = df['traveller_type'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['traveller_type'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})
plt.gca().set_xticklabels(df['traveller_type'], rotation=60, horizontalalignment= 'right')
plt.title("Traveller types in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
In [34]:
df = skytrax.groupby('cabin').size().reset_index(name='counts')
n = df['cabin'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)
# Plot Bars
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['cabin'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})
# Decoration
plt.gca().set_xticklabels(df['cabin'], rotation=60, horizontalalignment= 'right')
plt.title("Cabin class in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
Для параметров, отражающих оценки, здесь и далее я использовал один цвет столбцов на диаграмме (серый), чтобы они смотрелись как одно целое, а не как разные категории (как на других диаграммах)
In [35]:
df = skytrax.groupby('overall').size().reset_index(name='counts')
c = 'lightslategray'
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['overall'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
print(summ)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
plt.gca()
plt.title("Overall rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
64017.0 27.153724791852163 % 9.353765406064014 % 6.312385772529172 % 4.067669525282347 % 4.978365121764531 % 4.116094162488089 % 7.169970476592155 % 11.261071277941797 % 12.262367808550852 % 13.324585656934875 %
In [36]:
len(skytrax)
Out[36]:
65947
In [37]:
df = skytrax.groupby('seat_comfort').size().reset_index(name='counts')
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['seat_comfort'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
plt.gca()
plt.title("Seat comfort rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
25.085282048746727 % 13.549545986387832 % 20.004614294424943 % 23.785039798289414 % 17.575517872151085 %
In [38]:
df = skytrax.groupby('cabin_service').size().reset_index(name='counts')
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['cabin_service'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
print(summ)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
plt.gca()
plt.title("Cabin service rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
60715.0 24.145598287078975 % 12.04644651239397 % 14.637239561887508 % 18.822366795684754 % 30.348348842954785 %
In [39]:
df = skytrax.groupby('food_bev').size().reset_index(name='counts')
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['food_bev'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
plt.gca()
plt.title("Food beverage rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
27.448296836982966 % 13.543567518248176 % 18.67396593673966 % 21.41119221411192 % 18.922977493917276 %
In [40]:
df = skytrax.groupby('entertainment').size().reset_index(name='counts')
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['entertainment'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
plt.gca()
plt.title("Entertainment rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
30.39395379358722 % 11.504084357251148 % 18.140882040142102 % 21.292964949200098 % 18.66811485981943 %
In [41]:
df = skytrax.groupby('ground_service').size().reset_index(name='counts')
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['ground_service'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
print(summ)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
# Decoration
plt.gca()
plt.title("Ground service rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
39358.0 39.99186950556431 % 9.390721073225265 % 12.630214949946645 % 17.317953148025815 % 20.66924132323797 %
In [42]:
df = skytrax.groupby('value_for_money').size().reset_index(name='counts')
plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['value_for_money'], df['counts'], color=c, width=.5)
summ = 0
for i, val in enumerate(df['counts'].values):
summ+= float(val)
for i, val in enumerate(df['counts'].values):
print(float(val) / summ * 100, '%')
plt.gca()
plt.title("Value for money rating in reviews", fontsize=22)
plt.ylabel('# Reviews')
plt.show()
31.04650254005471 % 11.781164517389605 % 12.925361469323954 % 20.223524814380617 % 24.023446658851114 %
Почти во всех оценках превалируют единицы, как и в общей оценке. Это говорит о том, что люди чаще всего пишут оценки именно чтобы высказать что-то негативное. По этой причине их стоит анализировать в первую очередь для нахождения "слабых мест" авиакомпании
Рекомендации нагляднее изобразить в виде круговой диаграммы, а не в виде гистограммы
In [43]:
df = skytrax.groupby('recommended').size().reset_index(name='counts')
fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)
data = df['counts']
categories = df['recommended']
def func(pct, allvals):
absolute = int(pct/100.*np.sum(allvals))
return "{:.1f}% ({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(data,
autopct=lambda pct: func(pct, data),
textprops=dict(color="w"),
colors=plt.cm.Dark2.colors,
startangle=140)
ax.legend(wedges, categories, loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
plt.setp(autotexts, size=10, weight=700)
ax.set_title("Would you recommend this company?")
plt.show()
Перейдём к созданию модели.
Начнём с предобработки данных. Вначале уберём ненужные столбцы
In [44]:
df = skytrax.copy().reset_index(drop=True)
df.head()
Out[44]:
| airline | overall | author | review_date | customer_review | aircraft | traveller_type | cabin | route | date_flown | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Turkish Airlines | 7.0 | Christopher Hackley | 8th May 2019 | ✅ Trip Verified | London to Izmir via Istanb... | NaN | Business | Economy Class | London to Izmir via Istanbul | 2019-05-01 00:00:00 | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | yes |
| 1 | Turkish Airlines | 2.0 | Adriana Pisoi | 7th May 2019 | ✅ Trip Verified | Istanbul to Bucharest. We ... | NaN | Family Leisure | Economy Class | Istanbul to Bucharest | 2019-05-01 00:00:00 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
| 2 | Turkish Airlines | 3.0 | M Galerko | 7th May 2019 | ✅ Trip Verified | Rome to Prishtina via Ista... | NaN | Business | Economy Class | Rome to Prishtina via Istanbul | 2019-05-01 00:00:00 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | no |
| 3 | Turkish Airlines | 10.0 | Zeshan Shah | 6th May 2019 | ✅ Trip Verified | Flew on Turkish Airlines I... | A330 | Solo Leisure | Economy Class | Washington Dulles to Karachi | April 2019 | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | yes |
| 4 | Turkish Airlines | 1.0 | Pooja Jain | 6th May 2019 | ✅ Trip Verified | Mumbai to Dublin via Istan... | NaN | Solo Leisure | Economy Class | Mumbai to Dublin via Istanbul | 2019-05-01 00:00:00 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
In [45]:
del df['airline']
del df['author']
del df['review_date']
del df['customer_review']
del df['route']
del df['date_flown']
df.head()
Out[45]:
| overall | aircraft | traveller_type | cabin | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.0 | NaN | Business | Economy Class | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | yes |
| 1 | 2.0 | NaN | Family Leisure | Economy Class | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
| 2 | 3.0 | NaN | Business | Economy Class | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | no |
| 3 | 10.0 | A330 | Solo Leisure | Economy Class | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | yes |
| 4 | 1.0 | NaN | Solo Leisure | Economy Class | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | no |
Преобразуем целевую переменную в правильный формат
In [46]:
def recommended(row):
if (row['recommended'] == "yes"):
return 1
else:
return 0
df['recommended'] = df.apply(recommended, axis=1)
df.head()
Out[46]:
| overall | aircraft | traveller_type | cabin | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.0 | NaN | Business | Economy Class | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | 1 |
| 1 | 2.0 | NaN | Family Leisure | Economy Class | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 |
| 2 | 3.0 | NaN | Business | Economy Class | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | 0 |
| 3 | 10.0 | A330 | Solo Leisure | Economy Class | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1 |
| 4 | 1.0 | NaN | Solo Leisure | Economy Class | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 |
Затем построим матрицу корреляций признаков
In [47]:
corr = df.corr()
plt.figure(figsize=(15, 11))
sns.heatmap(corr, vmax=.8, square=True, cmap='magma')
/opt/anaconda3/lib/python3.7/site-packages/seaborn/matrix.py:268: PendingDeprecationWarning: The label function will be deprecated in a future version. Use Tick.label1 instead.
Out[47]:
<matplotlib.axes._subplots.AxesSubplot at 0x1256d43d0>
Затем посмотрим, в каких столбцах у нас есть пропуски и в каком количестве
In [48]:
clmns = df.columns[df.isnull().any()]
missed = pd.DataFrame(df[clmns].isnull().sum().sort_values(ascending=False) / df.shape[0], columns=['% NULL'])
missed
Out[48]:
| % NULL | |
|---|---|
| aircraft | 0.701002 |
| ground_service | 0.403187 |
| traveller_type | 0.397167 |
| entertainment | 0.329871 |
| food_bev | 0.202268 |
| seat_comfort | 0.079852 |
| cabin_service | 0.079336 |
| cabin | 0.040093 |
| value_for_money | 0.029903 |
| overall | 0.029266 |
Теперь обработаем категориальные параметры. Сделаем это вручную one-hot кодированием, потому что в них всех немного категорий (кроме aircraft, но об этом сейчас будет написано подробнее). Так мы сделаем эти параметры значимыми для модели, а также избавимся от пропусков в них (у тех строк, где были пропуски, просто будет 0 в этих столбцах)
In [49]:
cat_clmns = df.columns[df.dtypes == 'object']
df[cat_clmns].head()
Out[49]:
| aircraft | traveller_type | cabin | |
|---|---|---|---|
| 0 | NaN | Business | Economy Class |
| 1 | NaN | Family Leisure | Economy Class |
| 2 | NaN | Business | Economy Class |
| 3 | A330 | Solo Leisure | Economy Class |
| 4 | NaN | Solo Leisure | Economy Class |
Начнём с обработки моделей самолётов. Рассмотрим, какие там есть варианты
In [50]:
skytrax_aircraft = skytrax.groupby('aircraft').size().reset_index(name='counts')
def aircraft(row):
return row['counts'] / len(skytrax) * 100
skytrax_aircraft['%'] = skytrax_aircraft.apply(aircraft, axis=1)
skytrax_aircraft.sort_values('counts', inplace=True, ascending=False)
skytrax_aircraft = skytrax_aircraft.reset_index()
print(len(skytrax_aircraft))
skytrax_aircraft[:10]
2088
Out[50]:
| index | aircraft | counts | % | |
|---|---|---|---|---|
| 0 | 132 | A320 | 2157 | 3.270808 |
| 1 | 1290 | Boeing 777 | 1215 | 1.842389 |
| 2 | 651 | A380 | 1109 | 1.681653 |
| 3 | 377 | A330 | 1074 | 1.628581 |
| 4 | 1019 | Boeing 737-800 | 1036 | 1.570958 |
| 5 | 1575 | Boeing 787 | 934 | 1.416289 |
| 6 | 1487 | Boeing 777-300ER | 841 | 1.275267 |
| 7 | 73 | A319 | 689 | 1.044778 |
| 8 | 289 | A321 | 684 | 1.037197 |
| 9 | 942 | Boeing 737 | 660 | 1.000804 |
Всего вариантов модели самолёта очень много, причем многие из них похожи друг на друга. Поэтому не будем разбивать этот параметр на все возможные варианты, а возьмем самые популярные модели и создадим вручную параметры только для них. Так мы не только создадим новые значимые параметры, но ещё и сможем объединить в них некоторые некорректно заполненные данные или те, в которых мы отбросим ненужные подробности о модели
In [51]:
def A320(row):
if (row['aircraft'] != row['aircraft']):
return 0
if ((str(row['aircraft']).find("A320") != -1) or (str(row['aircraft']).find("A319") != -1) or (str(row['aircraft']).find("A321") != -1)):
return 1
else:
return 0
def Boeing777(row):
if (row['aircraft'] != row['aircraft']):
return 0
if (str(row['aircraft']).find("Boeing 777") != -1):
return 1
else:
return 0
def A380(row):
if (row['aircraft'] != row['aircraft']):
return 0
if (str(row['aircraft']).find("A380") != -1):
return 1
else:
return 0
def A330(row):
if (row['aircraft'] != row['aircraft']):
return 0
if (str(row['aircraft']).find("A330") != -1):
return 1
else:
return 0
def Boeing737(row):
if (row['aircraft'] != row['aircraft']):
return 0
if (str(row['aircraft']).find("Boeing 737") != -1):
return 1
else:
return 0
def Boeing787(row):
if (row['aircraft'] != row['aircraft']):
return 0
if (str(row['aircraft']).find("Boeing 787") != -1):
return 1
else:
return 0
df['A320'] = df.apply(A320, axis=1)
df['Boeing777'] = df.apply(Boeing777, axis=1)
df['A380'] = df.apply(A380, axis=1)
df['A330'] = df.apply(A330, axis=1)
df['Boeing737'] = df.apply(Boeing737, axis=1)
df['Boeing787'] = df.apply(Boeing787, axis=1)
del df['aircraft']
df.head()
Out[51]:
| overall | traveller_type | cabin | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | A320 | Boeing777 | A380 | A330 | Boeing737 | Boeing787 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.0 | Business | Economy Class | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 2.0 | Family Leisure | Economy Class | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 3.0 | Business | Economy Class | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 10.0 | Solo Leisure | Economy Class | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | 1.0 | Solo Leisure | Economy Class | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Теперь перейдём к остальным категориальным параметрам, там ситуация проще
In [52]:
set(df['traveller_type'])
Out[52]:
{'Business', 'Couple Leisure', 'Family Leisure', 'Solo Leisure', nan}
In [53]:
set(df['cabin'])
Out[53]:
{'Business Class', 'Economy Class', 'First Class', 'Premium Economy', nan}
In [54]:
def business_type(row):
if (row['traveller_type'] == "Business"):
return 1
else:
return 0
def couple(row):
if (row['traveller_type'] == "Couple Leisure"):
return 1
else:
return 0
def family(row):
if (row['traveller_type'] == "Family Leisure"):
return 1
else:
return 0
def solo(row):
if (row['traveller_type'] == "Solo Leisure"):
return 1
else:
return 0
df['business_type'] = df.apply(business_type, axis=1)
df['couple'] = df.apply(couple, axis=1)
df['family'] = df.apply(family, axis=1)
df['solo'] = df.apply(solo, axis=1)
del df['traveller_type']
df.head()
Out[54]:
| overall | cabin | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | A320 | Boeing777 | A380 | A330 | Boeing737 | Boeing787 | business_type | couple | family | solo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.0 | Economy Class | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 2.0 | Economy Class | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 3.0 | Economy Class | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 10.0 | Economy Class | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 1.0 | Economy Class | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
In [55]:
def business_class(row):
if (row['cabin'] == "Business Class"):
return 1
else:
return 0
def economy_class(row):
if (row['cabin'] == "Economy Class"):
return 1
else:
return 0
def first_class(row):
if (row['cabin'] == "First Class"):
return 1
else:
return 0
def prem_economy_class(row):
if (row['cabin'] == "Premium Economy"):
return 1
else:
return 0
df['business_class'] = df.apply(business_class, axis=1)
df['economy_class'] = df.apply(economy_class, axis=1)
df['first_class'] = df.apply(first_class, axis=1)
df['prem_economy_class'] = df.apply(prem_economy_class, axis=1)
del df['cabin']
df.head()
Out[55]:
| overall | seat_comfort | cabin_service | food_bev | entertainment | ground_service | value_for_money | recommended | A320 | Boeing777 | ... | Boeing737 | Boeing787 | business_type | couple | family | solo | business_class | economy_class | first_class | prem_economy_class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.0 | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 4.0 | 1 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| 2 | 3.0 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 2.0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 10.0 | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 4 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
5 rows × 22 columns
На всякий случай ещё раз посмотрим на корелляцию
In [56]:
corr = df.corr()
plt.figure(figsize=(15, 11))
sns.heatmap(corr, vmax=.8, square=True, cmap='magma')
Out[56]:
<matplotlib.axes._subplots.AxesSubplot at 0x12a685bd0>
Убедимся, что пропуски остались только в числовых параметрах
In [57]:
clmns = df.columns[df.isnull().any()]
missed = pd.DataFrame(df[clmns].isnull().sum().sort_values(ascending=False) / df.shape[0], columns=['% NULL'])
missed
Out[57]:
| % NULL | |
|---|---|
| ground_service | 0.403187 |
| entertainment | 0.329871 |
| food_bev | 0.202268 |
| seat_comfort | 0.079852 |
| cabin_service | 0.079336 |
| value_for_money | 0.029903 |
| overall | 0.029266 |
И заполним их медианными значениями
In [58]:
fill = df.apply(lambda s: s.mode()[0] if s.dtype == 'object' else s.median(), axis=0)
df = df.fillna(value=fill)
Теперь, когда с предобработкой окончено, перейдём к самому предсказанию. Я заметил, что можно попробовать предсказать отдельно суммарную оценку (overall) и то, рекомендует человек авиакомпанию или нет (recommended). Это и проделаем
In [59]:
y = df['overall']
In [60]:
X = df.copy()
del X['overall']
del X['recommended']
In [61]:
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
Для предсказания общей десятибальной оценки будем использовать линейную регрессию. Точность посчитаем по R2
In [62]:
lr = LinearRegression()
lr.fit(x_train, y_train)
test_p = lr.predict(x_test)
print('Test R2 %.3f\n' % r2_score(y_test, test_p))
Test R2 0.827
In [63]:
print(lr.coef_)
[ 0.30305748 0.36678423 0.2283083 0.12416473 0.39339426 1.04085887 0.21451791 0.08795063 0.0880591 -0.01505323 0.20560461 0.07433036 -0.50884338 -0.49583796 -0.52863518 -0.5059585 0.4440992 0.37980854 0.4355858 0.39678066]
In [64]:
for i in range(len(X.columns)):
print(X.columns[i], '%.3f' %lr.coef_[i])
seat_comfort 0.303 cabin_service 0.367 food_bev 0.228 entertainment 0.124 ground_service 0.393 value_for_money 1.041 A320 0.215 Boeing777 0.088 A380 0.088 A330 -0.015 Boeing737 0.206 Boeing787 0.074 business_type -0.509 couple -0.496 family -0.529 solo -0.506 business_class 0.444 economy_class 0.380 first_class 0.436 prem_economy_class 0.397
In [65]:
for i in range(len(X.columns)):
print(X.columns[i])
seat_comfort cabin_service food_bev entertainment ground_service value_for_money A320 Boeing777 A380 A330 Boeing737 Boeing787 business_type couple family solo business_class economy_class first_class prem_economy_class
In [66]:
stats.summary(lr, x_train, y_train)
Residuals:
Min 1Q Median 3Q Max
-9.873 -0.8212 -0.1004 0.7528 8.9334
Coefficients:
Estimate Std. Error t value p value
_intercept -2.220390 0.032377 -68.5783 0.000000
x1 0.303057 0.007416 40.8649 0.000000
x2 0.366784 0.006829 53.7066 0.000000
x3 0.228308 0.007303 31.2613 0.000000
x4 0.124165 0.006539 18.9874 0.000000
x5 0.393394 0.006566 59.9169 0.000000
x6 1.040859 0.007051 147.6197 0.000000
x7 0.214518 0.027110 7.9128 0.000000
x8 0.087951 0.029203 3.0117 0.002599
x9 0.088059 0.042609 2.0667 0.038771
x10 -0.015053 0.035046 -0.4295 0.667538
x11 0.205605 0.037496 5.4834 0.000000
x12 0.074330 0.043932 1.6919 0.090665
x13 -0.508843 0.023934 -21.2604 0.000000
x14 -0.495838 0.020475 -24.2165 0.000000
x15 -0.528635 0.022981 -23.0029 0.000000
x16 -0.505959 0.018218 -27.7729 0.000000
x17 0.444099 0.030969 14.3400 0.000000
x18 0.379809 0.024707 15.3728 0.000000
x19 0.435586 0.051962 8.3827 0.000000
x20 0.396781 0.042815 9.2674 0.000000
---
R-squared: 0.82993, Adjusted R-squared: 0.82985
F-statistic: 10775.75 on 20 features
/opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
А вероятность рекомендации предскажем с помощью логистической регрессии. В качестве меры точности посчитаем precision
In [67]:
y = df['recommended']
In [68]:
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
lr = LogisticRegression()
lr.fit(x_train, y_train)
test_p = lr.predict(x_test)
print('Test Precision %.3f\n' %precision_score(y_test, test_p, average='macro'))
/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
Test Precision 0.938
In [69]:
p = stats.coef_pval(lr, x_train, y_train)
print('%18s %18s %18s' %("feature", "coef", "p-value"))
for i in range(len(X.columns)):
print('%18s %18s %18s' %(X.columns[i], '%.3f' %math.exp(lr.coef_[0][i]), '%.3f' %p[i]))
feature coef p-value
seat_comfort 1.600 0.000
cabin_service 2.013 0.000
food_bev 1.439 0.000
entertainment 1.228 0.000
ground_service 1.948 0.000
value_for_money 4.705 0.000
A320 2.376 0.000
Boeing777 1.133 0.000
A380 0.999 0.000
A330 1.207 0.874
Boeing737 2.041 0.000
Boeing787 1.523 0.000
business_type 0.836 0.000
couple 0.616 0.000
family 0.630 0.000
solo 0.842 0.000
business_class 10.648 0.000
economy_class 9.279 0.000
first_class 9.679 0.000
prem_economy_class 8.846 0.000
/opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
In [70]:
dff = df[df['A320'] == 1].groupby('recommended').size().reset_index(name='counts')
fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)
data = dff['counts']
categories = dff['recommended']
def func(pct, allvals):
absolute = int(pct/100.*np.sum(allvals))
return "{:.1f}% ({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(data,
autopct=lambda pct: func(pct, data),
textprops=dict(color="w"),
colors=plt.cm.Dark2.colors,
startangle=140)
ax.legend(wedges, categories, loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
plt.setp(autotexts, size=10, weight=700)
ax.set_title("Would you recommend this company?")
plt.show()
Результаты получились очень хорошие даже у простых моделей. Возможно, потому что в данных было много корреляций
In [ ]:
3) Анкетирование¶
Данные: https://www.kaggle.com/teejmahal20/airline-passenger-satisfaction
В целом столбцы похожи на те, что были у Skytrax, только с большим количеством информации о пассажирах (возраст, пол), а также более подробными характеристиками полета (большее количество параметров оценок, а также задержка в минутах и длина рейса в километрах)
In [71]:
survey_train = pd.read_csv('train.csv')
survey_test = pd.read_csv('test.csv')
survey_train.head()
Out[71]:
| Unnamed: 0 | id | Gender | Customer Type | Age | Type of Travel | Class | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | ... | Inflight entertainment | On-board service | Leg room service | Baggage handling | Checkin service | Inflight service | Cleanliness | Departure Delay in Minutes | Arrival Delay in Minutes | satisfaction | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 70172 | Male | Loyal Customer | 13 | Personal Travel | Eco Plus | 460 | 3 | 4 | ... | 5 | 4 | 3 | 4 | 4 | 5 | 5 | 25 | 18.0 | neutral or dissatisfied |
| 1 | 1 | 5047 | Male | disloyal Customer | 25 | Business travel | Business | 235 | 3 | 2 | ... | 1 | 1 | 5 | 3 | 1 | 4 | 1 | 1 | 6.0 | neutral or dissatisfied |
| 2 | 2 | 110028 | Female | Loyal Customer | 26 | Business travel | Business | 1142 | 2 | 2 | ... | 5 | 4 | 3 | 4 | 4 | 4 | 5 | 0 | 0.0 | satisfied |
| 3 | 3 | 24026 | Female | Loyal Customer | 25 | Business travel | Business | 562 | 2 | 5 | ... | 2 | 2 | 5 | 3 | 1 | 4 | 2 | 11 | 9.0 | neutral or dissatisfied |
| 4 | 4 | 119299 | Male | Loyal Customer | 61 | Business travel | Business | 214 | 3 | 3 | ... | 3 | 3 | 4 | 4 | 3 | 3 | 3 | 0 | 0.0 | satisfied |
5 rows × 25 columns
Посмотрим корелляцию признаков
In [72]:
survey_train.columns
Out[72]:
Index(['Unnamed: 0', 'id', 'Gender', 'Customer Type', 'Age', 'Type of Travel',
'Class', 'Flight Distance', 'Inflight wifi service',
'Departure/Arrival time convenient', 'Ease of Online booking',
'Gate location', 'Food and drink', 'Online boarding', 'Seat comfort',
'Inflight entertainment', 'On-board service', 'Leg room service',
'Baggage handling', 'Checkin service', 'Inflight service',
'Cleanliness', 'Departure Delay in Minutes', 'Arrival Delay in Minutes',
'satisfaction'],
dtype='object')
In [73]:
corr = survey_train.corr()
plt.figure(figsize=(15, 11))
sns.heatmap(corr, vmax=.8, square=True, cmap='magma')
/opt/anaconda3/lib/python3.7/site-packages/seaborn/matrix.py:268: PendingDeprecationWarning: The label function will be deprecated in a future version. Use Tick.label1 instead.
Out[73]:
<matplotlib.axes._subplots.AxesSubplot at 0x126817f50>
Склеим пока тест и трейн, чтобы обработать все признаки
In [74]:
survey = pd.concat([survey_train, survey_test], ignore_index=True)
survey.head()
Out[74]:
| Unnamed: 0 | id | Gender | Customer Type | Age | Type of Travel | Class | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | ... | Inflight entertainment | On-board service | Leg room service | Baggage handling | Checkin service | Inflight service | Cleanliness | Departure Delay in Minutes | Arrival Delay in Minutes | satisfaction | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 70172 | Male | Loyal Customer | 13 | Personal Travel | Eco Plus | 460 | 3 | 4 | ... | 5 | 4 | 3 | 4 | 4 | 5 | 5 | 25 | 18.0 | neutral or dissatisfied |
| 1 | 1 | 5047 | Male | disloyal Customer | 25 | Business travel | Business | 235 | 3 | 2 | ... | 1 | 1 | 5 | 3 | 1 | 4 | 1 | 1 | 6.0 | neutral or dissatisfied |
| 2 | 2 | 110028 | Female | Loyal Customer | 26 | Business travel | Business | 1142 | 2 | 2 | ... | 5 | 4 | 3 | 4 | 4 | 4 | 5 | 0 | 0.0 | satisfied |
| 3 | 3 | 24026 | Female | Loyal Customer | 25 | Business travel | Business | 562 | 2 | 5 | ... | 2 | 2 | 5 | 3 | 1 | 4 | 2 | 11 | 9.0 | neutral or dissatisfied |
| 4 | 4 | 119299 | Male | Loyal Customer | 61 | Business travel | Business | 214 | 3 | 3 | ... | 3 | 3 | 4 | 4 | 3 | 3 | 3 | 0 | 0.0 | satisfied |
5 rows × 25 columns
In [75]:
del survey['Unnamed: 0']
del survey['id']
Обработаем опять таки категориальные признаки в нужный нам формат
In [76]:
cat_clmns = survey.columns[survey.dtypes == 'object']
survey[cat_clmns].head()
Out[76]:
| Gender | Customer Type | Type of Travel | Class | satisfaction | |
|---|---|---|---|---|---|
| 0 | Male | Loyal Customer | Personal Travel | Eco Plus | neutral or dissatisfied |
| 1 | Male | disloyal Customer | Business travel | Business | neutral or dissatisfied |
| 2 | Female | Loyal Customer | Business travel | Business | satisfied |
| 3 | Female | Loyal Customer | Business travel | Business | neutral or dissatisfied |
| 4 | Male | Loyal Customer | Business travel | Business | satisfied |
In [77]:
set(survey['Gender'])
Out[77]:
{'Female', 'Male'}
In [78]:
set(survey['Customer Type'])
Out[78]:
{'Loyal Customer', 'disloyal Customer'}
In [79]:
set(survey['Type of Travel'])
Out[79]:
{'Business travel', 'Personal Travel'}
In [80]:
set(survey['Class'])
Out[80]:
{'Business', 'Eco', 'Eco Plus'}
In [81]:
set(survey['satisfaction'])
Out[81]:
{'neutral or dissatisfied', 'satisfied'}
In [82]:
def female(row):
if (row['Gender'] == "Female"):
return 1
else:
return 0
def male(row):
if (row['Gender'] == "Male"):
return 1
else:
return 0
survey['male'] = survey.apply(female, axis=1)
survey['female'] = survey.apply(male, axis=1)
del survey['Gender']
survey.head()
Out[82]:
| Customer Type | Age | Type of Travel | Class | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | Ease of Online booking | Gate location | Food and drink | ... | Leg room service | Baggage handling | Checkin service | Inflight service | Cleanliness | Departure Delay in Minutes | Arrival Delay in Minutes | satisfaction | male | female | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Loyal Customer | 13 | Personal Travel | Eco Plus | 460 | 3 | 4 | 3 | 1 | 5 | ... | 3 | 4 | 4 | 5 | 5 | 25 | 18.0 | neutral or dissatisfied | 0 | 1 |
| 1 | disloyal Customer | 25 | Business travel | Business | 235 | 3 | 2 | 3 | 3 | 1 | ... | 5 | 3 | 1 | 4 | 1 | 1 | 6.0 | neutral or dissatisfied | 0 | 1 |
| 2 | Loyal Customer | 26 | Business travel | Business | 1142 | 2 | 2 | 2 | 2 | 5 | ... | 3 | 4 | 4 | 4 | 5 | 0 | 0.0 | satisfied | 1 | 0 |
| 3 | Loyal Customer | 25 | Business travel | Business | 562 | 2 | 5 | 5 | 5 | 2 | ... | 5 | 3 | 1 | 4 | 2 | 11 | 9.0 | neutral or dissatisfied | 1 | 0 |
| 4 | Loyal Customer | 61 | Business travel | Business | 214 | 3 | 3 | 3 | 3 | 4 | ... | 4 | 4 | 3 | 3 | 3 | 0 | 0.0 | satisfied | 0 | 1 |
5 rows × 24 columns
In [83]:
def loyal(row):
if (row['Customer Type'] == "Loyal Customer"):
return 1
else:
return 0
def disloyal(row):
if (row['Customer Type'] == "disloyal Customer"):
return 1
else:
return 0
survey['loyal'] = survey.apply(loyal, axis=1)
survey['disloyal'] = survey.apply(disloyal, axis=1)
del survey['Customer Type']
survey.head()
Out[83]:
| Age | Type of Travel | Class | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | Ease of Online booking | Gate location | Food and drink | Online boarding | ... | Checkin service | Inflight service | Cleanliness | Departure Delay in Minutes | Arrival Delay in Minutes | satisfaction | male | female | loyal | disloyal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13 | Personal Travel | Eco Plus | 460 | 3 | 4 | 3 | 1 | 5 | 3 | ... | 4 | 5 | 5 | 25 | 18.0 | neutral or dissatisfied | 0 | 1 | 1 | 0 |
| 1 | 25 | Business travel | Business | 235 | 3 | 2 | 3 | 3 | 1 | 3 | ... | 1 | 4 | 1 | 1 | 6.0 | neutral or dissatisfied | 0 | 1 | 0 | 1 |
| 2 | 26 | Business travel | Business | 1142 | 2 | 2 | 2 | 2 | 5 | 5 | ... | 4 | 4 | 5 | 0 | 0.0 | satisfied | 1 | 0 | 1 | 0 |
| 3 | 25 | Business travel | Business | 562 | 2 | 5 | 5 | 5 | 2 | 2 | ... | 1 | 4 | 2 | 11 | 9.0 | neutral or dissatisfied | 1 | 0 | 1 | 0 |
| 4 | 61 | Business travel | Business | 214 | 3 | 3 | 3 | 3 | 4 | 5 | ... | 3 | 3 | 3 | 0 | 0.0 | satisfied | 0 | 1 | 1 | 0 |
5 rows × 25 columns
In [84]:
def business_type(row):
if (row['Type of Travel'] == "Business travel"):
return 1
else:
return 0
def personal(row):
if (row['Type of Travel'] == "Personal Travel"):
return 1
else:
return 0
survey['business_type'] = survey.apply(business_type, axis=1)
survey['personal'] = survey.apply(personal, axis=1)
del survey['Type of Travel']
survey.head()
Out[84]:
| Age | Class | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | Ease of Online booking | Gate location | Food and drink | Online boarding | Seat comfort | ... | Cleanliness | Departure Delay in Minutes | Arrival Delay in Minutes | satisfaction | male | female | loyal | disloyal | business_type | personal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13 | Eco Plus | 460 | 3 | 4 | 3 | 1 | 5 | 3 | 5 | ... | 5 | 25 | 18.0 | neutral or dissatisfied | 0 | 1 | 1 | 0 | 0 | 1 |
| 1 | 25 | Business | 235 | 3 | 2 | 3 | 3 | 1 | 3 | 1 | ... | 1 | 1 | 6.0 | neutral or dissatisfied | 0 | 1 | 0 | 1 | 1 | 0 |
| 2 | 26 | Business | 1142 | 2 | 2 | 2 | 2 | 5 | 5 | 5 | ... | 5 | 0 | 0.0 | satisfied | 1 | 0 | 1 | 0 | 1 | 0 |
| 3 | 25 | Business | 562 | 2 | 5 | 5 | 5 | 2 | 2 | 2 | ... | 2 | 11 | 9.0 | neutral or dissatisfied | 1 | 0 | 1 | 0 | 1 | 0 |
| 4 | 61 | Business | 214 | 3 | 3 | 3 | 3 | 4 | 5 | 5 | ... | 3 | 0 | 0.0 | satisfied | 0 | 1 | 1 | 0 | 1 | 0 |
5 rows × 26 columns
In [85]:
def business_class(row):
if (row['Class'] == "Business"):
return 1
else:
return 0
def eco(row):
if (row['Class'] == "Eco"):
return 1
else:
return 0
def eco_plus(row):
if (row['Class'] == "Eco Plus"):
return 1
else:
return 0
survey['business_class'] = survey.apply(business_class, axis=1)
survey['eco'] = survey.apply(eco, axis=1)
survey['eco_plus'] = survey.apply(eco_plus, axis=1)
del survey['Class']
survey.head()
Out[85]:
| Age | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | Ease of Online booking | Gate location | Food and drink | Online boarding | Seat comfort | Inflight entertainment | ... | satisfaction | male | female | loyal | disloyal | business_type | personal | business_class | eco | eco_plus | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13 | 460 | 3 | 4 | 3 | 1 | 5 | 3 | 5 | 5 | ... | neutral or dissatisfied | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 1 | 25 | 235 | 3 | 2 | 3 | 3 | 1 | 3 | 1 | 1 | ... | neutral or dissatisfied | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
| 2 | 26 | 1142 | 2 | 2 | 2 | 2 | 5 | 5 | 5 | 5 | ... | satisfied | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 3 | 25 | 562 | 2 | 5 | 5 | 5 | 2 | 2 | 2 | 2 | ... | neutral or dissatisfied | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 4 | 61 | 214 | 3 | 3 | 3 | 3 | 4 | 5 | 5 | 3 | ... | satisfied | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
5 rows × 28 columns
In [86]:
def satisfaction(row):
if (row['satisfaction'] == "satisfied"):
return 1
else:
return 0
survey['satisfaction'] = survey.apply(satisfaction, axis=1)
survey.head()
Out[86]:
| Age | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | Ease of Online booking | Gate location | Food and drink | Online boarding | Seat comfort | Inflight entertainment | ... | satisfaction | male | female | loyal | disloyal | business_type | personal | business_class | eco | eco_plus | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13 | 460 | 3 | 4 | 3 | 1 | 5 | 3 | 5 | 5 | ... | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 1 | 25 | 235 | 3 | 2 | 3 | 3 | 1 | 3 | 1 | 1 | ... | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
| 2 | 26 | 1142 | 2 | 2 | 2 | 2 | 5 | 5 | 5 | 5 | ... | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 3 | 25 | 562 | 2 | 5 | 5 | 5 | 2 | 2 | 2 | 2 | ... | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 4 | 61 | 214 | 3 | 3 | 3 | 3 | 4 | 5 | 5 | 3 | ... | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
5 rows × 28 columns
Посмотрим, остались ли где-то ещё пробелы в данных
In [87]:
clmns = survey.columns[survey.isnull().any()]
missed = pd.DataFrame(survey[clmns].isnull().sum().sort_values(ascending=False) / survey.shape[0], columns=['% NULL'])
missed
Out[87]:
| % NULL | |
|---|---|
| Arrival Delay in Minutes | 0.003026 |
Заполним их медианными данными
In [88]:
fill = survey.apply(lambda s: s.mode()[0] if s.dtype == 'object' else s.median(), axis=0)
survey = survey.fillna(value=fill)
А вот теперь сделаем сами модели. Предсказывать опять таки будем общую удовлетворённость, которая у нас в формате yes/no (0/1)
In [89]:
y = survey['satisfaction']
X = survey.copy()
del X['satisfaction']
Начнём с логистической регрессии. Результат precision неплохой, но можно получить лучше
In [90]:
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
lr = LogisticRegression()
lr.fit(x_train, y_train)
test_p = lr.predict(x_test)
print('Test Precision %.3f\n' %precision_score(y_test, test_p, average='macro'))
/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
Test Precision 0.872
In [91]:
p = stats.coef_pval(lr, x_train, y_train)
print('%26s %26s %26s' %("feature", "coef", "p-value"))
for i in range(len(X.columns)):
print('%26s %26s %26s' %(X.columns[i], '%.3f' %math.exp(lr.coef_[0][i]), '%.3f' %p[i]))
feature coef p-value
Age 0.992 1.000
Flight Distance 1.000 0.000
Inflight wifi service 1.511 0.944
Departure/Arrival time convenient 0.884 0.000
Ease of Online booking 0.836 0.000
Gate location 1.029 0.000
Food and drink 0.970 0.000
Online boarding 1.829 0.000
Seat comfort 1.080 0.000
Inflight entertainment 1.058 0.000
On-board service 1.373 0.000
Leg room service 1.274 0.000
Baggage handling 1.138 0.000
Checkin service 1.387 0.000
Inflight service 1.116 0.000
Cleanliness 1.240 0.000
Departure Delay in Minutes 1.004 0.000
Arrival Delay in Minutes 0.991 0.000
male 0.213 0.000
female 0.227 1.000
loyal 0.608 1.000
disloyal 0.080 1.000
business_type 0.890 1.000
personal 0.054 1.000
business_class 0.597 1.000
eco 0.306 1.000
eco_plus 0.264 1.000
/opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/regressors/stats.py:144: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. /opt/anaconda3/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py:71: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
Перейдём к более сложным моделям, попробуем catboost с некоторыми большими параметрами
In [92]:
model = cb.CatBoostRegressor(iterations=5000, depth=7)
model.fit(x_train, y_train)
test_p = model.predict(x_test)
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In [93]:
print('Test Precision %.3f\n' %precision_score(y_test, test_p.round(), average='macro'))
Test Precision 0.964
In [94]:
for i in range(len(X.columns)):
print(X.columns[i], '%.3f' %model.get_feature_importance()[i])
Age 2.138 Flight Distance 1.255 Inflight wifi service 23.873 Departure/Arrival time convenient 0.839 Ease of Online booking 0.935 Gate location 3.786 Food and drink 0.276 Online boarding 10.039 Seat comfort 2.299 Inflight entertainment 4.651 On-board service 2.603 Leg room service 1.601 Baggage handling 3.126 Checkin service 2.753 Inflight service 2.645 Cleanliness 1.453 Departure Delay in Minutes 0.400 Arrival Delay in Minutes 0.809 male 0.070 female 0.014 loyal 5.045 disloyal 4.218 business_type 9.949 personal 10.296 business_class 4.722 eco 0.142 eco_plus 0.062
In [95]:
df = pd.DataFrame({'import': model.feature_importances_, 'feat': X.columns})
df = df.sort_values(['import', 'feat'], ascending=[True, False])
df.plot(kind='barh', x='feat', y='import', figsize=(12, 10), legend=None)
plt.ylabel('Features')
plt.xlabel('Importance')
Out[95]:
Text(0.5, 0, 'Importance')
Теперь попробуем xgboost с определенным набором параметров
In [96]:
import xgboost as xgb
/opt/anaconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
In [97]:
dtrain = xgb.DMatrix(x_train, label=y_train)
dtest = xgb.DMatrix(x_test, label=y_test)
param = {
'max_depth': 8,
'eta': 0.3,
'silent': 1,
'objective': 'multi:softprob',
'num_class': 2}
num_round = 20
bst = xgb.train(param, dtrain, num_round)
preds = bst.predict(dtest)
best_preds = np.asarray([np.argmax(line) for line in preds])
print('Test Precision %.3f\n' %precision_score(y_test, best_preds, average='macro'))
Test Precision 0.962
In [98]:
bst.get_score(importance_type='cover')
Out[98]:
{'Online boarding': 4386.298750319997,
'Inflight wifi service': 2220.7477708102874,
'Inflight entertainment': 1046.4731136096063,
'business_class': 2083.6605425312514,
'Ease of Online booking': 684.3682939191256,
'business_type': 2308.85345425589,
'loyal': 1181.513192731547,
'Flight Distance': 264.9962954296132,
'Checkin service': 1135.472716309832,
'Baggage handling': 695.6097753941528,
'Inflight service': 621.8839831620897,
'Cleanliness': 936.4944757425462,
'Gate location': 551.9206484357926,
'Departure Delay in Minutes': 194.42019225330878,
'Age': 378.96248405101784,
'Leg room service': 676.5763017853247,
'Arrival Delay in Minutes': 539.3315599790191,
'On-board service': 961.1188463434423,
'Departure/Arrival time convenient': 217.13789277354851,
'Seat comfort': 762.4166966815734,
'eco_plus': 778.213989,
'Food and drink': 165.29658019226198,
'male': 39.86095493333333,
'eco': 286.43277120000005}
In [99]:
df = pd.DataFrame({'import': np.array(list(bst.get_score(importance_type='cover').values())), 'feat': np.array(list(bst.get_score().keys()))})
df = df.sort_values(['import', 'feat'], ascending=[True, False]).iloc[-30:]
df.plot(kind='barh', x='feat', y='import', figsize=(12, 10), legend=None)
plt.ylabel('Features')
plt.xlabel('Importance')
Out[99]:
Text(0.5, 0, 'Importance')
Получаем, что catboost лучше справился с задачей, хотя оба результата очень точные
В совокупности набор визуализирующих графиков и эти модели создают хороший инструмент для анализа удовлетворённости клиентов. Причём по Твиттеру это может быть ежедневная картина с почасовым изменением активности, по Skytrax это полная карта характеристик полёта в виде гистограмм по тысячам отзывов, а по анкетированию это наиболее точное предсказание удовлетворённости клиентов
In [ ]: